Localized prostate cancer is a very heterogeneous disease, from both a clinical and a biological/biochemical point of view, which makes the task of producing stratifications of patients into risk classes remarkably challenging. In particular, it is important an early detection and discrimination of the indolent forms of the disease, from the aggressive ones, requiring post-surgery closer surveillance and timely treatment decisions. This work extends a recently developed supervised machine learning (ML) technique, called coherent voting networks (CVN) by incorporating a novel model-selection technique to counter the danger of model overfitting. For the challenging problem of discriminating between indolent and aggressive types of localized prostate cancer, accurate prognostic prediction of post-surgery progression-free survival with a granularity within a year is attained, improving accuracy with respect to the current state of the art. The development of novel ML techniques tailored to the problem of combining multi-omics and clinical prognostic biomarkers is a promising new line of attack for sharpening the capability to diversify and personalize cancer patient treatments. The proposed approach allows a finer post-surgery stratification of patients within the clinical high-risk category, with a potential impact on the surveillance regime and the timing of treatment decisions, complementing existing prognostic methods. The ECIS—European Cancer Information System (https://ecis.jrc.ec.europa.eu) predicts an incidence of 363,000 new diagnosed PRC for the EU27 + EFTA area in the year 2025 and estimates a mortality of about 78,000 due to PRC . Siegel et al.1 report an estimate of 268,490 new cases of diagnosed prostate cancer for the year 2022 in the USA, and an estimate of 34,500 deaths due to prostate cancer . About 15% of the localized prostate cancer diagnoses are clinically classified as high risk2 and thus require timely management decisions. This work concentrates on this fraction of the PRC patient population.According to the World Cancer Research Fund International web site (4. This report describes the application of a recently developed machine learning (ML) technique, called coherent voting networks to the problem of predicting an accurate prognosis for patients affected by prostate cancer (PRC).Prostate cancer is a very heterogeneous disease, from both a clinical and a biological/biochemical point of view, which makes the task of producing a stratification of patients into risk classes particularly challenging. In particular, it is important an early detection and discrimination of the indolent forms of the disease, from the aggressive ones, requiring closer surveillance5. Since CVN is a general ML technique it is natural to extend such a technique to handling further cancer types , further data type , different time points and different events of interest. Moreover, in this report, the CVN technique is further developed in order to provide additional theoretical grounding to some of the algorithmic phases involved. Specifically, the hyper-parameter optimization and feature selection phases (collectively indicated as model selection) are re-examined and a variation of the method by Andrew Ng6 to cope with model overfitting is shown to be both well grounded from a theoretical point of view and effective on PRC data. To the best of my knowledge, the application of Ng’s theory for prognostic purposes is new.Coherent voting networks (CVN) have been developed for the task of predicting the overall survival (OS) of breast cancer (BC) patients at 5 years after surgery, based on tissue transcriptomic fingerprints (mRNA), and depending on the specific adjuvant therapy adoptedhttps://www.cancer.gov/about-nci/organization/ccg/research/structural-genomics/tcga).The improved CVN is used to develop multi-gene fingerprints for predicting the risk of Progress-free survival (PFS) of patients over several time points. The molecular data sets for the fingerprint discovery are provided by the TCGA consortium and consist of assays of prostate biopsies and tissue removed via radical prostatectomy in patients diagnosed with prostate adenocarcinoma, who had not received prior treatment for their disease , Gleason score, Tumor stage, and other clinical measures often fail to distinguish between indolent and aggressive tumors, thus leading to over-diagnosis and over-treatment11 list at least 32 prognostic genic signatures for PRC, however only a handful have been thoroughly validated, and made into commercially available kits . Such commercial kits are increasingly included in clinical protocols and practice17.As of 2021, Manjang et al.This report contributes to the search for effective multi-gene prognostic fingerprinting by applying the CVN to several omic data sets from prostate cancer patients with the aim of learning a pool of effective fingerprints. Next, such fingerprints are applied to independent cohorts data sets to assess how performant these fingerprints can be . The reported results show remarkable promising performances in terms of Odds Ratio, Cohen’s kappa, and AUC, with good statistical significance, and a fine time resolution of 1 year. Moreover it is shown that standard clinical-pathological biomarkers can be combined with genomic biomarkers to improve predictive performance.This paper is organized as follows. Section “The TCGA-PRAD dataset is used for training, validating, and testing the prognostic CVN in the discovery phase and determining the best performing multi-gene fingerprints. In Supplementary Table In Supplementary Table Overall, due to the randomized split of the patients, these features have similar distributions  over the patient groups.In Supplementary Table 18 , thus consistent with the specimens used in the discovery cohort. Two independent cohorts (GSE37199 and GSE53922) are based instead on blood samples of PRC patients. Unexpectedly, fingerprint Fp20 has significant discriminative power also on both of these test cases, however the number of no answers increases to 30–40% of the patient cohort.Additional performance measures including confidence intervals, PPV/NPV, and Sensitivity/Specificity are in the GitHub project repository.From the TCGA PRAD clinical data file 24 clinical/pathological features known to have prognostic power in prostate cancer were selected. These features were then appended to the omic molecular expression matrices and the fingerprint discovery pipeline was iterated. The fingerprint fp160 composed of three clinical parameters: Gleason primary score, tumor stage, psa, and two molecular protein expression levels for CDKN1B and NF2 has emerged as very concise and performant. Performance measures are reported in Table A series of complementary tests and searches have been performed in order to support the novelty, relevance, and robustness of the proposed prognostic fingerprints and algorithms in the context of prostate cancer. In Supplementary Materials Section As research in prognostic predictions, in general, and for prostate cancer, in particular, is a vast subject with implications from several areas of biology and medicine, here comments on the relationship of this work with some issues arising in the relevant literature are given. Each issue is introduced by a short heading.19 and Goldenberg et al.20 review recent advances in biomarker discovery for prostate cancer, indicating ML-based and AI-based approaches as opening a new dimension to research and opportunities for transferring new computational techniques in clinical practice in this area. This work pushes this view by extending the novel ML paradigm of the Coherent Voting Networks (CVN) with improved model selection techniques, and by applying it to the challenging problem of the prognosis of prostate cancer at a fine time granularity (year-to-year).Alarcón-Zendejas et al.21. Such proteomic data, although less abundant than mRNA expression data may have the advantage of representing a more accurate snapshot of the cell’s biological processes. This study has derived two fingerprints from mRNA data, three from proteomic data, one mixed with mRNA and proteomic data, and one from methylation data.This study uses mainly mRNA gene expression data sets obtained via high throughput assays as the primary source for prognostic biomarker discovery and validation. This technology is now mature and, over time, data on many cohorts of patients have become publicly available. The results on mRNA-based fingerprints appear to be robust w.r.t the specific technology used for measuring mRNA levels of expression. Interestingly, some of the best reported results are obtained from proteomic data obtained with Reverse-Phase Protein microArrays (rppa) assays23. Toth et al.24 attain good prognostic performance with a Random Forest algorithm, to discriminate patients according to eventual recurrence-free survival as an outcome, measured by PSA levels. However, the model they describe requires input from a large number of methylation sites . The methylation-based fingerprint comprises just six methylation loci with performance validated in the independent GSE84042 methylation data set.Many studies indicate that changes in DNA methylation contribute to cancer development and regulation. Cancers characteristically display extensive hypomethylation of DNA repeats as well as frequent focal DNA hypermethylation25, although the majority of studies still uses tissue-derived microRNA26. Experiments with microRNA data from the TCGA-PRAD cohort did produce fingerprints with statistically significant but suboptimal performance (data not shown) vs. those obtained via mRNA, rppa (Reverse Phase Protein Array), and methylation data. Similarly, statistically significant but suboptimal results were obtained with TCGA-PRAD CNA and microbiome data (data not shown). Smith and Sheltzer27 study the prognostic power of CNA in several cancer types, including prostate cancer, focusing on alterations of known driver genes. They used Cox proportional hazards analysis, concluding that very few mutations were significantly associated with patient outcomes. Their analyses suggested that, in general, cancer driver gene mutations lacked significant patient stratification power. The results in this study on the TCGA-PRAD CNA are consistent with these findings.MicroRNAs have been investigated as potential biomarkers for PRC prognosis as they can be derived also from liquid biopsies28 study the patterns of differentially expressed genes in normal adjacent prostate tissue (NAP), benign prostatic hyperlasia (BPH), localized prostate cancer, and metastatic, hormone-refractory prostate cancer, using unsupervised hierarchical clustering. Among the genes cited28 as strongly correlated with the above classification, two genes (MYC and CDH1) are also present in the selected fingerprints. Rhodes et al.29 produced a list of genes consistently up-regulated or down-regulated in several cohorts of prostate cancer patients with clinically localized prostate cancer versus benign prostate tissue. In this list, MYC is found but no other gene in the selected fingerprints. The inference is that, in all likelihood, the fingerprints in this study do not target the known PRC subtypes per se, but, instead, aim directly at the tracking the relevant biological process in tumor’s development . A hybrid approach is using genomic profiling to reduce the technical and subjective variability in the estimation of well-known clinical/histological parameters. For example, Wang et al.31 initially identify the candidate genes related to the Gleason score, then these genes are used to construct a LASSO Cox regression prognostic analysis model based on a 3 genes fingerprint . Predictors composed of mixed clinical and omic features were also considered, finding good and performance, confirmed in independent cohorts, for a fingerprint composed of three well known clinical parameters  and expression levels for NF2 and CDKN1B. Interestingly these three clinical parameters were not pre-determined, but emerged from a pool of 24 clinical features. Moreover, the fact that these three clinical features are already routinely collected in practice, implies that just two additional ’omic’ expression measurements need to be collected (possibly by RT-PCR). Integration of clinical and genomic fingerprints has been shown to be beneficial also for the Decipher fingerprint32.Historically, histological and clinical parameters have been extensively studied in order to provide effective prognostic stratification of PRC patients. This line of research is now being supplemented with AI-based techniques. For example, Guinney et al.In the discovery cohort TCGA-PRAD, no patient received neo-adjuvant therapies prior to surgery/biopsy. About a quarter of the patients has a record of some treatment after surgery , which may have been administered after monitoring revealed the progress of the disease. Since the aim of this study is at predicting the duration of progression-free survival (PFS), and treatment data was not complete, no stratification of patients into treatment classes has been done. Moreover, note that this study is retrospective and the effect of personalized therapeutic choices can be detected more reliably within randomized clinical trials specifically designed for this objective.33 recently compiled a systematic review of diagnostic and prognostic biomarkers in prostate cancer, with emphasis on those likely to progress towards clinical practice. The proposed multi-gene biomarker fingerprints may be useful within the prostate cancer management work-flow as a PRC risk stratification decision point, following a prostate biopsy/surgery, thus it can be hypothesized a potential future use akin to that of the current kits such as Promark, Oncotype Dx, Prolaris, and Decipher.Beyer et al.34 study in-depth the class of localized, non-indolent prostate cancer and propose a multi-modal pool of biomarkers to predict disease relapse as indicated by BCR . Interestingly, their method was effective in predicting eventual relapse with AUC 0.83 34). However, when it was applied to detect early relapse (relapse by month 18) it did not perform well (log-rank test p = 0.14) 34). In contrast, the proposed signatures are effective within the first 2–5 years since surgery/biopsy, with 1-year resolution. Most of the proposed fingerprints are composed of one molecular type, except fp20 which is composed of two, and fp160 composed of clinical and genomic (Reverse Phase Protein Array) markers. Several recent studies have focused their attention on providing refined risk stratifications in the early years after primary treatment. Fu et al.35 propose an 18-genes genomic fingerprint for prediction of recurrence with AUC performance values of 0.747, 0.827, and 0.851 respectively after 1-, 3-, and 5-years from surgery in the GSE46602 independent cohort. Zhou et al.36 report prognostic accuracies for 3- and 5-year BCR-free survival of AUC 0.68 and 0.713, respectively, for a 26-patient independent cohort. Results reported in Tables Fraser et al.38. In particular issues relative to PRC multiclonality and inter-tumor heterogeneity may limit the use of tissue biopsies as a source of reliable prognostic tests39. These issues may be mitigated in blood samples. Testing the selected fingerprints on independent cohorts with data from blood samples (GSEGSE53922 and GSE3719), it was found that one fingerprint (fp20) retains prognostic power also in both of these cohorts, although with a higher percentage of no answers. As the biological and transcriptional interplay of primary prostate adenocarcinoma with eventual bone metastasis affecting several components of blood is complex and not well-understood38, I expect that better results may be obtained by using blood samples  directly as the target for the biomarkers discovery phase.Blood samples have several advantages with respect to tumor tissue samples as biospecimen of choice for prognostic purposes, and several blood-based prognostic signatures have been proposed for prostate cancerOne independent cohort (GSE53922) is composed mainly of patients at the stage of Castration-Resistant Prostate Cancer (CRPC). It was found that fingerprints fp14, fp20, and fp30 are prognostic with good performance also for this sub-class of PRC patients, although, in this case, further data is likely needed to confirm this finding. For fp20 also partial support comes from the result on cohort GSE37199 where fp20 can discriminate CRPC from the indolent form of local PRC.Many of the genes in the eight fingerprints been studied individually for their role in cancer (of any type), and they affect functionally important cancer biological processes, as determined via knock-out experiments in cell lines and/or animal models of cancer. In some cases, their gene expression is directly modulated by a microRNA with an important role in cancer progression. Although this is not yet sufficient to establish causal relationships between the expression of these genes and tumor development, it is a good stepping stone towards a more complex type of analysis that integrates bio-networks and causality relationships more explicitly in the model.The main limitation in the current state of the CVN methodology is that the biomarker discovery phase is based on the trisection of the discovery cohort into training, validation, and testing sets , while the performance of the selected model can be measured reliably only on the testing set. Thus the size of the discovery cohort needs to be rather large in order for the testing set to be sufficient to attain statistical significance. It is an open line of research to extend the model-selection phase to reach statistical robustness with fewer initial samples.One natural question is whether, among methylation, mRNA, proteomic, and mixed fingerprints, one data type outperforms the others in the context of prostate cancer prognosis. The answer to this question is mostly dependent on trade-offs across different concerns. For example, data from Table 5. Novel algorithmic features described below include a model selection module based on a theory by Andrew Ng for avoiding model overfitting, and the implementation of a bootstrapping module in a train-test setting according to the work by Efron and Tibshirani18.Supplementary Fig. https://www.cbioportal.org) ). The procedures for sample selection and processing are described in detail in the paper by Abeshouse et al.3 and its Supplementary files. Briefly, surgical resection biospecimens were collected from patients at the participating institutions diagnosed with prostate adenocarcinoma, who had not received prior treatment for their disease . The specimens comprise primary tumor tissue, normal solid tissue, and blood-derived normal. Pathology quality control was performed on each tumor and normal tissue specimen from a frozen section slide. Hematoxylin and eosin (H &E) stained sections from each sample were subjected to independent pathology review to confirm that the tumor specimen was histologically consistent with the allowable prostate adenocarcinoma subtypes and the adjacent normal specimen contained no tumor cells. Computational pipelines include batch effect analysis and correction. Note that this study uses only the primary tumor-tissue data and clinical data. Some technical details of the data acquisition technologies are summarized in Supplementary Table 40 show that TCGA PRAD data for PFS is of high quality and can be safely used for prognostic purposes. A synopsis of independent cohort’s patient features is in the “The discovery cohort is the TCGA-PRAD (2018) data set downloaded from cbioportal  as high-risk, and progression-free survival above t takes consecutive integer values 2, 3, 4 and 5; and each specific time frame is denoted with the pair Extending the methodology in Pellegrini5 and applied to the classification of breast cancer patients into prognostic survival categories  after surgical removal of the tumor5. The Coherent Voting Network is designed explicitly to uncover non-linear, combinatorial patterns in complex data, within a statistically robust framework. Moreover, the coherent voting communities mechanism can be seen as a ’post hoc’ result explanation approach, providing a certificate justifying the survival prediction for an individual patient, thus facilitating its acceptability in practice, in the vein of explainable Artificial Intelligence (See discussion in “The Coherent Voting Network (CVN) is a supervised learning algorithm introduced by Pellegrini5 [Supplementary Materials].In a nutshell, CVN can be seen as a generalization of the notion of guilt by association (GbA) in biological networks, where an unlabeled patient node receives a predicted label by collecting the vote of many dense communities of labeled patients and genes to which the unlabeled patient node belongs. The CVN algorithm also seeks a minimal number of genes with the property of allowing a coherent vote of high accuracy on the labeled nodes, and thus such a minimal set represents arguably a good candidate fingerprint to be performing well also on predictions for the unlabeled nodes. A schematic depiction of the workflow for the main CVN algorithm is in Supplementary Fig. model-selection phase.As in many complex ML paradigms, the CVN depends on a number of inner parameters, and thus it is important to do properly both feature selection (i.e. the selection of the fingerprint genes) and hyper-parameter optimization. These two tasks are called together the The input cohort of patients is split randomly into a training set, a validation set, and a test set . Then the algorithm proceeds in three phases. In Phase I the CVN is applied to the training set  in order to produce a list of candidate gene fingerprints . In phase II, the candidate fingerprints, the training set, and the validation set  are used together to do model-selection and fix both the fingerprint and the hyper-parameter configuration that minimizes the generalization error (or other performance target measures). Finally, in Phase III the single selected CVN is applied to the test set to measure the effective generalization error. The test set is a set of patients not used in phases I and II, thus unlikely to suffer from overfitting.5 noticed that the standard model selection method suffers from a particular type of overfitting discovered by Ng6 as an effect of having a large number of hypotheses to choose from. This issue was solved by introducing a Pareto stratification5 of the models, and by using the notion of a limited and controlled lookup of test data during the model-selection phase (phase II). The lookahead number 1 corresponds to the standard model selection, while it was considered acceptable also lookahead numbers less or equal to 4, thus overfitting is prevented by using a controlled information leak.PellegriniThe fingerprints so selected were next further validated in independent cohorts of cancer patients, thus showing that the Pareto-based model selection did perform well empirically.6, as described in the next section. In practice, both the Pareto-based model selection and the Ng-based model selection are used to attain the results shown in this paper.The main technical contribution of this paper is a new look at the problem of model selection by generalizing and expanding the approach proposed by Ng5.Missing data and censored patients are handled as described in detail in Pellegrini6 it is described the following phenomenon. One has many predictive models (hypotheses) to choose from and uses cross-validation on a pool of validation data in order to select the hypothesis minimizing the cross-validation error, as a representing a hypothesis hopefully minimizing also the generalization error . Ng shows that when the number of hypotheses to choose from is large a form of over-fitting occurs so that the hypothesis minimizing the cross-validation error is a poor predictor of the generalization error. Next, an algorithm called LOOCVCV is proposed to cope with this phenomenon6. LOOCVCV is based on estimating the number H of hypotheses has the minimum expected misclassification error. Having the estimate of H hypotheses.In NgOptimization of the expected generalization value of functions different from the generalization error, in particular Cohen’s kappa measure (and variations of it).H by using lexicographic sorting of the value of a function paired with the index of the hypothesis.Simplification of the handling of ties in the ranking of Skipping the index-scaling approach to the hypothesis selection by recording in the computation process of the estimate of 6.probabilities of events are computed exactly via binomial coefficients, not in a quick but approximate fashionThe LOOCVCV method is modified and generalized in four aspects. 41. Finally, a mixed version is considered that uses the second function for a number of no answers below 15% and the third version when the number of no answers is above 15%. These four measures are all in the range The presence of possible no-predictions introduces some complications, as the Cohen kappa can be changed in several different ways. Four versions of the kappa function differing in the way they handle the no answers are computed. The first solution is to apply the standard Cohen’s kappa functional just ignoring the no answers. The second solution is to scale the first solution by the fraction of predictions. The third solution is to apply Gwet’s version of kappa5  the Pareto-based model selection process is described in detail. Here we give a summary to compare it with the Ng-based selection procedure. Each configuration of CVN on the Validating set is mapped to a point in 3D space representing its performance profile , where the quality score is either Cohen’s kappa or the Odds Ratio. Duplicated points are removed. For this set of points, the optimal  Pareto front is computed, and then the computation is iterated on the residual set. This process produces a Pareto stratification of the points. Within each stratum, the points are sorted by the quality score. This produces a total ordering of the points. Next, using this order, we compare the quality score obtained on the Validation set and the Test set for corresponding configurations. This comparison stops when either the Test quality score is better than the Validation quality score, or it is within a relative displacement of 0.2. The number of such comparisons is the lookahead number (lh) and it measures the controlled information leakage we allow to balance the performances of the validation and test sets. Not that lh lh numbers In Ref.mentclass2pt{minim42.The independent cohorts used to validate the chosen fingerprints are smaller than the TCGA-PRAD cohort used to discover them. Therefore splitting these data into three sets risks producing results lacking statistical significance just due to the small numbers involved. For this reason, a different common machine learning paradigm is applied: the leave-one-out (LOO) approach to hyper-parameter optimization (now the features—genes—are fixed), and bootstrapping to evaluate the quality of the chosen configuration18 can be adopted. In particular, notice that the formula for the leave-one-out bootstrap error estimation  can be applied to obtain smoothed estimates of any function that is a sum (linear combination) of the single error indicator functions for the elements of the testing set. Therefore bootstrap estimates of the relevant quantities: TP (True Positive), FP , TN (True Negative), FN , and NA (No Answers) can be made with this approach. From these values, estimates of the bootstrapped odds ratio and kappa are computed. Note that the area under the curve (AUC) does not have the required functional form for the application of the theory18. All the prediction maps produced in the bootstrap process are collected and for each patient in the input set a consensus prediction is produced that is the majority of the predictions in the collections of bootstrap maps. Finally, the AUC of the consensus prediction map is computed using the equivalence to the Wilcoxon-Mann-Whitney U-Statistic.Bootstrapping is a very general technique with deep theoretical support and extensive practical applications. In the context of cross-validation, the formalism by Efron and Tibshiranin items is done by sampling uniformly at random with replacement43 for the correction to the theories need in this case). Note that the only practical effect of sampling in the context of this study is to partition the input set into an in-set and an out-set For the bootstrapping experiments, the value B of bootstrap iterations.In standard bootstrapping the sampling in a set of entclass1pt{minimaPatients were not directly involved in the study. All data used in this study is in the public domain and was obtained with the appropriate consent.This report has two main contributions. From the methodological point of view, the CVN (Coherent Voting Network) paradigm is extended by providing a novel robust model selection technique to overcome the danger of overfitting, inspired by a method of Andrew Ng. Next, the improved CVN methodology is applied to tackle the problem of stratifying prostate cancer patients in risk classes (for adverse events within 2–5 years from surgery/biopsy). Several candidate genomic fingerprints are produced to cover different time-frames at a 1-year resolution using of different omic data  and clinical data. These multi-gene fingerprints can help in deciding the monitoring regime to be applied to prostate cancer patients, within an established clinical decision process. Many of the biomarkers in the proposed pool of genes are known individually as cancer hallmark genes or they are shown functionally involved in cancer using animal models or cell lines. The proposed fingerprints appear to be robust in tests with several independent cohorts. However, the task of measuring the proposed biomarkers in an accurate, reproducible, and cost-effective way for a clinical setting (e.g. via RT-PCR) is left as future research.Supplementary Information.


Artificial intelligence (AI) is a rapidly evolving field of computer science that involves the development of computational programs that can mimic human intelligence. In particular, machine learning and deep learning models have enabled the identification and grouping of patterns within data, leading to the development of AI systems that have been applied in various areas of hematology, including digital pathology, alpha thalassemia patient screening, cytogenetics, immunophenotyping, and sequencing. These AI-assisted methods have shown promise in improving diagnostic accuracy and efficiency, identifying novel biomarkers, and predicting treatment outcomes. However, limitations such as limited databases, lack of validation and standardization, systematic errors, and bias prevent AI from completely replacing manual diagnosis in hematology. In addition, the processing of large amounts of patient data and personal information by AI poses potential data privacy issues, necessitating the development of regulations to evaluate AI systems and address ethical concerns in clinical AI systems. Nonetheless, with continued research and development, AI has the potential to revolutionize the field of hematology and improve patient outcomes. To fully realize this potential, however, the challenges facing AI in hematology must be addressed and overcome. Artificial intelligence (AI) refers to computer programs that contain robust datasets and simulate human intelligence, including problem-solving skills and learning . The conMachine learning (ML) is a subset of AI that enables computers to learn without any preset rules. It is performed by utilizing algorithms developed to process the dataset to automatically detect patterns and perform tasks, such as detection and classification, in various domains . FurtherDeep learning (DL) is a powerful subset of ML which refers to a multilayered network of artificial neurons aimed at creating models from raw data. The expanding amount of data provided by wearables, smartphones, and other mobile monitoring sensors can be handled by DL algorithms in a number of medical fields. The algorithms are separated into layers to create an artificial neural network (ANN) which functions similarly to the human brain . ANNs coThese AI ML and DL models are the foundation of AI systems applied in hematology. In recent years, researchers have utilized these models to develop algorithms for automated interpretation of blood cell morphology, classification of blood disorders, prediction of disease prognosis, and identification of new biomarkers. For instance, automated blood cell morphology analysis using DL models has shown promising results in reducing inter-observer variability and improving diagnostic accuracy . MoreoveBy leveraging the power of AI and ML models, researchers are making significant progress in the field of hematology. Given the increased prevalence of hematologic disorders and emerging global pandemics, these models have the potential to revolutionize hematology by providing faster and more accurate diagnoses, improving patient prognosis, and lowering healthcare costs. However, there are still challenges that need to be addressed, such as the need for large datasets and the potential for bias in the training data. Therefore, further research and development are necessary to fully realize the potential of AI in hematology. The most recent updates from well-known electronic databases, including PubMed, the Web of Science, Medline, and Google Scholar, and search engines for gray literature such as Google, were meticulously combed through. Conference abstracts were excluded while we considered full-text, peer-reviewed publications, books, review papers, and preprints pertinent to the area and written in English. This review highlights the current updates and challenges in the role of AI in diagnosing hematologic disorders, risk stratification, prediction of disease prognosis, challenges, and future directions in implementing AI in the health system.Non-communicable diseases are becoming a global health problem, resulting in the death of 41 million people each year and accounting for 74% of all deaths globally . Cancer,Current diagnosis of hematologic malignancies is based on evaluating patient blood parameters, biopsies to test small pieces of tissue for cancerous cells, and imaging tests to rule out metastasis of specific disease conditions. The treatment options may include any combination of chemotherapy, bone marrow transplantation, immunotherapy, radiation therapy, targeted therapy, and transplantation . The emeReview of peripheral blood smears (PBSs) and bone marrow smears (BMSs) is a powerful diagnostic tool that provides rapid, reliable information about a variety of disorders such as leukemia, anemia, infections, and allergies . Smear rThe use of AI in hematology is still limited, with only a few Food and Drug Administration (FDA)-approved AI devices currently available on the market. These devices are primarily used for the examination of PBS morphology. Among them, CellaVision and Morphogo are two representative systems used for the examination of PBS and BMA slides, respectively. CellaVision is an automated digital image analyzer that has been approved by the FDA ,20. It cThe pre-classification of both white blood cells (WBCs) and red blood cells (RBCs) is achieved by means of ML-based ANNs, which are a collection of connected artificial neurons (nodes) organized into hidden layers that can perform different transformations based on the inputs received. Signals are transmitted from the input to the output layer, possibly passing through the hidden layers multiple times . CellaViRBCs are pre-characterized by a program called CellaVision Advanced RBC application, which captures them using a 50× oil-immersion lens. RBCs are classified based on morphological abnormalities such as size, central pallor, roundness, border notching, and inclusion bodies . They caThe potential of AI in hematologic diagnosis is undeniable because it offers several benefits, such as reduced turnaround time and improved consistency in patient results due to easier standardization. CellaVision can enhance the training efficiency of staff, as well as reduce eyestrain by allowing laboratory technologists to review and verify results on a computer screen. However, this technology has limitations that prevent it from fully replacing traditional slide reviews by manual microscopy. CellaVision has low sensitivity and specificity in identifying lymphoblasts and plasma cells, which share similar morphological features with lymphocytes. In cases of leukemia, CellaVision can identify blast cells, but cannot differentiate myeloblasts, lymphoblasts, and monoblasts. Additionally, CellaVision has poor sensitivity and specificity in identifying schistocytes, which are crucial for the diagnosis of microangiopathic hemolytic anemia (MAHA). According to the guidelines of the International Council for Standardization in Hematology, even the presence of <1% schistocytes in a PBS should be reported for the diagnosis of thrombotic thrombocytopenic purpura and hemolytic uremic syndrome . HoweverBMS examination plays a crucial role in diagnosing hematologic diseases. Although manual microscopy remains the gold standard, it is time consuming and can be subject to bias among hematologists. Morphogo is an AI-aided BMA smear analysis system that can differentiate nucleated cells into specific categories and provide information useful for diagnosis. It employs a 27-layered CNN that captures multiple high-resolution images of the BMS and pieces them together to generate a full picture of the target morphology . The conOn the other hand, the Morphogo AI-based system was able to diagnose technically challenging metastatic non-hematopoietic tumor cells in BMSs. For instance, the method can classify metastatic cancer cells with 82.2% accuracy and 91.3% specificity. The study indicated that Morphogo could be a reliable method (reliability coefficient of 0.827) in the detection and classification of cells of cancer cell clusters (area under the curve (AUC): 0.865) . FurtherScopio is a newly FDA-approved high-throughput hematology digital cell morphology platform using a browser-based application. This advanced computational photography imaging tool with AI support enables the capture of digital scans with a full field of view of the monolayer and a feathered edge at a 100× oil-immersion magnification level. The system can hold up to 30 slides and process up to 40 samples within an hour, which can fulfill the high-throughput requirements of large hospitals and labs. It can pre-classify WBCs, estimate platelets, and evaluate the morphology of RBCs. According to a study by Katz et al. in 2021 , the ScoAnother AI-assisted blood cell analysis system, Mantiscope, uses AI to fully classify blood cells and find abnormalities. A scanner and the cloud are two components of the in vitro diagnostics system. The blood smear is automatically digitized using the scanner. Following this, the system analyzes the smear samples using AI after uploading the images to the cloud based on the patient’s barcode. Medical professionals can edit the AI recommendations using the system’s annotation interface. Additionally, the system can count, identify, and pre-classify cells in BMAs .Vision Hema provides automatic analysis and pre-classification of blood cells derived from a PBS. The system analyzes WBCs, RBCs, platelets, and reticulocytes in great detail. It also analyzes pathological changes in cells such as those with degenerative changes, blasts, atypical lymphocytes, erythroblasts, smudge cells, and other non-WBC cells. Modules for cell identification and pre-classification in bone marrow samples, body fluids, and cervical cytology are also available .It was a challenge to detect atypical and dysmorphic cells in patients with hematologic disorders, such as myelodysplastic syndromes (MDS), where their diagnosis is based on morphological findings in peripheral blood and bone marrow, particularly with other conditions presenting similar phenomena such as aplastic anemia (AA). Kimura et al. developed a novel AI-supported image analysis platform based on deep convolutional neural networks and CNNs, which can assist in differentiating between MDS and AA . They evnucleophosmin 1 mutation status from a BMS (AUC: 0.92) [Similarly, the use of DL helps to identify subtypes of acute myeloid leukemia (AML). Acute promyelocytic leukemia (APL) is a subclass of AML and is characterized by a translocation of the retinoic acid receptor alpha gene located on chromosome 17. Bleeding and thrombosis are major causes of fatal complications and death in patients with APL ,38. A muC: 0.92) .Diagnosis of lymphoma based on clinico-morphological features is challenging due to the heterogeneity of the malignant cells, time-consuming process, and requirement of expertise. Integration of AI-based models, such as CNNs, in the differential diagnosis of lymphoma helps to deliver a high standard of care and further improve the therapy of lymphoma patients. Automatic classification of common lymphoma types was improved via the use of pattern detection mechanisms based on the content of each image . EfficieSimilarly, a DL-based CNN approach helps to diagnose lymphoma from cytochemical-stained smears. They collected H&E-stained slides from 128 cases. Using WSIs, they established a diagnostic model for the four lymphoma groups, including benign lymph node, DLBCL, Burkitt lymphoma, and small lymphocytic lymphoma. Accordingly, they could predict the different types of lymphoma from H&E-stained images with an accuracy of 95% using the CNN model . Syrykh The ML-based method LymphoML can predict lymphoma subtypes from H&E-stained slides. The new method helps to predict the different types of lymphoma, including DLBCL (F1 score: 78.7%), classic Hodgkin lymphoma (F1 score: 74.5%), and mantle cell lymphoma (MCL) (F1 score: 71.0%), where the F1 score is a combined measure of both specificity and sensitivity. Considering the nuclear shape features, the method performed better with a diagnostic accuracy of more than 85% .Immunohistochemical staining (IHC), a more advanced type of histopathology, is an immunologic technique that uses antigen–antibody interactions to detect cellular or tissue antigens under a microscope . The tecAnemia can cause a variety of physical symptoms as well as visual signs, making it difficult to diagnose. As a result, a novel AI approach based on ML as a model is recommended for diagnosing, selecting treatments, and predicting prognosis . ThalassLee and colleagues developed an AI-based protocol for the detection of Hb-H inclusions that promises to improve the accuracy and efficiency of screening . The proIn order to further clarify the previous sentence, it is crucial to point out that the low PCT of the AI model in Hb-H inclusion detection may not necessarily point to a problem with the model itself but rather with the type of input data used. Kensert et al. suggested that the high similarity in image patterns between Hb-H-positive and -negative cells might pose a challenge for the AI model to accurately classify the cells . This hiAside from its use in advancing morphology-based diagnosis, ML can also be applied to other Hb variants, such as Hb-S and Hb-D, which require precise diagnosis and prediction in the diagnosis of hemoglobinopathies. These Hb variants can be identified using high-performance liquid chromatography (HPLC); however, Hb-S and Hb-D cannot always be reliably distinguished from one another due to differences in the software used for gradient programs and HPLC models. The use of ML, such as ANNs, helps to classify and predict Hb variants. Ucucu et al. demonstrated ANN-based recognition of human Hb variants in the HPLC system. The study used clinical samples of known hemoglobinopathies and found that ML was able to detect Hb variants with an accuracy of 99%, specificity of 99%, and sensitivity of 99% . This imFlow cytometry is a powerful laboratory assay for the detection, diagnosis, and monitoring of various hematologic disorders such as leukemia and lymphoma . While sSimilarly, Vial and colleagues developed a DL-based method to accurately detect MRD in AML using multiparameter flow cytometry (MFC) data. These studies demonstrate the potential of AI in enhancing the accuracy and efficiency of flow cytometric analysis in identifying MRD in AML. In this novel approach, flow cytometry results are transformed into a self-organized map (SOM) that is then converted into a two-dimensional (2D) format that is accessible by CNNs. The SOM file of the sample is then input into a CNN, which generates a prediction of MRD, implying that the use of AI allows for an easy and robust assessment of AML-MRD patients in the absence of molecular markers .Integration of ML in flow cytometry analysis helps to classify B-cell NHL (B-NHL). Gaidano et al. integrated ML into the available database of 1465 B-NHL samples. Accordingly, they were able to identify nine clinico-pathologic known subtypes of B-NHL with an accuracy of 92.7%, which includes DLBCL, Burkitt lymphoma, FCL, splenic lymphoma, MCL, marginal zone lymphoma (MZL), and lymphoplasmacytic lymphoma (LPL) . Zhao etRecent studies explored the use of AI in other areas of flow cytometric analysis beyond disease classification. Studies indicated that an AI-based method for automated gating of cell populations in flow cytometry data achieves high accuracy and consistency across different datasets . In addiUnderstanding computation data and applying them for patient diagnosis and management is a hurdle in the healthcare setting. DL tools could overcome this limit by interpreting the AI prediction system. The use of the “local interpretable model-agnostic explanations” algorithm for explainable artificial intelligence (XAI) helps to interpret the AI data with an accuracy of 98.4% . AI is bFluorescence in situ hybridization (FISH) has been used for detecting chromosomal abnormality by applying a DNA-targeted probe; however, the existing technique is time consuming and technically demanding. Although the DNA FISH model is the technique of choice to locate the genomic loci in a single allele, it requires user monitoring, which limits managing big data in a short time. The use of AI-integrated, FISH-based diagnosis of chromosomal abnormality will shorten the time required and improve the diagnosis. Gudla et al. developed a more efficient and accurate CNN-based DNA FISH detection method named SpotLearn. Accordingly, this CNN-based method could detect FISH signals with an accuracy greater than 98% . DeepSpoCytogenetic karyotyping is a well-established method for detecting chromosome abnormalities in patients. However, the process of preparing and interpreting the results can be time-consuming and requires experienced technologists . To addrTo address these challenges, new AI models were developed such as the ChromoEnhancer model developed by Bokhari and colleagues in 2022 . This moKaryotype analysis from G-banded metaphase images has important clinical significance in the diagnosis, treatment, and prognosis of hematologic tumors. Accordingly, CNNs perform well when it comes to image recognition. The metaphase system was originally used for chromosomal analysis in cells from different tissue types, e.g., lymphocytes and bone marrow. Hu et al. developed an AI-based CNN to detect chromosomal changes automatically. They used Softmax activation function mapping to classify chromosomes using a CNN with multiple layers that was trained using the labeled dataset. The results showed that the CNN-based identification of chromosomes was 93.8% accurate . On the Similarly, DL, such as DNNs, uses advanced algorithms in the field of AI applied for detecting and classifying chromosomal abnormalities. Through advanced AI applications, Ikaros, supported by DNN, was used in separating and classifying banded chromosomes. This system applied fluorescence R-banding to obtain all karyograms from bone marrow and blood samples. The DNN predicted the chromosome class and the required rotation angle from the individual chromosome image. This CNN-based application helped to predict chromosome classes with 98% accuracy and classify chromosome bands as normal and abnormal in hematologic malignancies. It further reduced the processing time by 42% when compared to the conventional karyotyping workflow  Figure .The use of AI in cytogenetic karyotyping holds great promise for improving the accuracy and efficiency of diagnosis. However, more studies are required to address some of the issues with this strategy, including the requirement for large datasets for building AI models and the shortcomings of weak AI classifiers that still require histological classification or medical examination. Overall, the development of advanced AI models, such as ChromoEnhancer, represents an important step toward improving the diagnosis and treatment of hematologic disorders ,94.BCR-ABL1 (breakpoint cluster region-Abelson 1) fusion gene in chronic myeloid leukemia (CML) and different gene expressions in thalassemia. Therefore, genetic testing, such as sequencing, has become a common diagnostic method [AI has been applied primarily in image-based diagnosis. However, accurate and precise disease classification is challenging with this approach. To address the difficulties in hematologic malignancies, a comprehensive strategy is necessary. Genetic profiling is crucial for the management of hematologic neoplasms because it provides important data for diagnosis, risk stratification, therapeutic choices, monitoring of residual disease, prognosis, and treatment resistance. Hematologic diseases, especially hematologic malignancies, are characterized by unique gene expression profiles, such as the c method . Howeverc method ,94.To address this issue, a research team developed a platform that combines a middle-throughput gene expression assay and ML to identify the subtyping of B-NHLs. Using a panel of 137 genetic markers, the gene expression assay classified B-NHLs according to their cellular origin, the composition of their microenvironment, and the configurations of their immunoglobulin genes . The teaThe conventional classification of leukemia, such as AML, is based on a clinicomorphopathologic classification, which groups AML into primary and secondary AML types. This kind of classification lacks correlation to molecular signatures. ML integrated genomic signatures for AML and was able to identify novel genomic AML subclasses. The model included genomic data from primary AML and secondary AML patients and applied both supervised and unsupervised ML methods. This ML model showed a 97% accuracy in classifying the different subtypes of AML and predicting their prognosis .These studies suggest the possibility of integrating AI into routine clinical testing by combining genetic profiling with limited laboratory resources, such as resources for DNA library construction only. The use of well-trained AI can avoid bias and human error in diagnosis since the classifier achieves a high accuracy of diagnosis based on historical databases. However, the limitations of using AI were uncovered, including the requirement for a larger database, such as data from patients with rare conditions, to enhance the accuracy of diagnosis and avoid missing rare cases. All AI classifiers mentioned in the studies were weak AI, which still requires histological classification or further examination by physicians for some cases. Therefore, the ultimate goal would be to develop a DL diagnostic system that does not require any historical database support or secondary examination ,97.ZMAT4 gene had a strong association with hematologic malignancies such as AML [Accurate copy number variation (CNV) detection is still a major issue for the community because of the unique characteristics of tumor samples and the complicated nature of tumor genomes. The collection and evaluation of representative data from various regions of the genome is also required for CNV interpretation, making it difficult to provide consistent, high-quality clinical interpretation of CNVs. The present diagnostic strategy employing next-generation sequencing requires excellent-quality data, which is not always the case owing to the lack of standards and the presence of numerous biases. Furthermore, the analysis of big data takes time and affects the quality of care in addition to the data quality. Advances in genomics and AI led to the development of new approaches for the diagnosis and treatment of hematologic disorders. One such approach involves the use of AI to analyze whole-genome sequencing (WGS) data for the detection of CNVs. CNVs are a form of genetic mutation that can contribute to the development and progression of hematologic malignancies. For instance, detecting CNVs in the h as AML . The gaih as AML , and gaih as AML . Furtherh as AML . Although as AML , detectiA study was published using AI to develop a DL model called AI-CN. AI-CN could accurately detect and classify CNVs in WGS data from patients with hematologic malignancies. Haferesh et al. reported that AI-CN could classify chromosome bands with an accuracy of 98.6% . CNV-P iIn another study, researchers used a combination of WGS and AI to identify CNVs in patients with AML . The resThese studies demonstrate the potential of AI-assisted WGS for CNV analysis in hematologic disorders. Using AI to analyze sequencing data, researchers can improve the accuracy and efficiency of CNV detection, which could lead to better prediction of patient diagnosis, therapeutic responses, and prognosis. The development of AI algorithms for CNV detection has the potential to revolutionize the field of hematologic disorders, enabling the development of more personalized and effective treatment strategies.Single-cell sequencing is a powerful tool that allows researchers to study individual cells within a heterogeneous population, providing valuable insights into complex disease mechanisms at the cellular level . In receAnother study used AI to analyze scRNA-seq data from patients with MDS. The researchers developed a DL model called DeepMDS, which was able to accurately predict patient outcomes based on gene expression profiles. They found that DeepMDS outperformed other prediction models in terms of accuracy and identified several novel biomarkers that were associated with disease progression .Additionally, AI can assist in the analysis of single-cell sequencing data from patients with AML. A DL model called support vector machine (SVM) was developed, which identified cell subpopulations and predicted patient outcomes based on gene expression profiles. They found that SVM outperformed other clustering and prediction methods in terms of accuracy and identified several genes that were associated with disease progression ,114. RecThere are emerging AI-assisted single-cell sequencing platforms that will be furthered via validation using clinical samples from patients with hematologic disorders. A multi-level convolutional neural network (MulCNN) was developed by Jiao and his colleagues to provide a unique, single-cell gene expression profile by extracting critical features through multi-scale convolution while filtering noise . BERMUDASimilarly, reference component analysis 2 (RCA2) uses reference transcriptomes as a guide and adopts graph-based clustering . It offers user-friendly downstream analysis modules, new mouse and human reference panels, and support for the establishment of custom panels. It also provides cell type-specific quality control for accurate clustering of data from heterogeneous sources. The method was evaluated on single-cell data from human bone marrow and healthy peripheral blood mononuclear cells (PBMCs) . AlthougDue to their capacity to offer solutions for diverse biological samples, scRNA-seq techniques are growing in popularity. Thought should be given to the difficulties with single-cell technologies when using new ML approaches to handle single-cell sequencing data. Limitations in benchmarking analysis tools, integration of data across models, types of cells and measurements, varying levels of resolution, handling of cell sparsity including a high dropout rate leading to the absence of expression, mapping single cells to a reference atlas, generalizing trajectory inferences, dealing with errors, and missing data are a few of the challenges, which impede downstream analysis and influence the performance of emerging ML models in the diagnosis of hematologic neoplasms ,122. TheEpigenetics is caused by a complex interaction between a person’s genotype and the environment, which plays a role in disease development. Recent studies showed the potential of AI-assisted epigenetic profiling for hematologic disorders. For instance, researchers utilized AI to analyze DNA methylation data from patients with ALL. The developed DL model, called single-cell omics references (EpiScore), accurately predicted patient outcomes based on DNA methylation patterns, outperforming other prediction models. EpiScore also identified novel biomarkers that were associated with disease progression, showing the potential of AI in enhancing our understanding of ALL ,124.In another study, a DL model called MethylNet was developed to analyze DNA methylation data from patients with MDS. The AI model accurately classified patients into different subtypes of MDS based on their DNA methylation patterns, outperforming other classification methods. Furthermore, MethylNet identified several novel biomarkers associated with disease progression, suggesting its potential for improving the diagnosis and treatment of MDS ,126.MM, a heterogeneous malignant tumor, is distinguished by abnormal plasma cell clonal proliferation in the bone marrow and is often accompanied by apparent monoclonal immunoglobulin protein. DNA methylation analysis in MM patients showed heterogeneity associated with transcriptomic variability, implying the need for more accurate prediction models ,128. A sOther epigenetic prediction tools, including DeepCpG , are alsThese studies highlight the potential of AI in enhancing our understanding of hematologic disorders by analyzing epigenetic data. By using AI to analyze DNA methylation and chromatin accessibility data, researchers can identify novel biomarkers associated with disease progression, ultimately leading to better diagnosis and treatment for patients.Although AI-based ML improved diagnostic accuracy and identification of potential biomarkers for epigenetic alterations, there are still limitations that need to be considered to improve the implementation of ML in clinical diagnosis. One potential challenge might arise from the low incidence of cases and increased differentially methylated regions (DMR) within a single case. Further, it has been indicated that epigenetic datasets have more variables than patient samples, limiting the effectiveness of AI-based algorithms. An analysis of a DNA methylation dataset reveals nonlinear relationships in addition to the DMR. Several CpG sites may appear on the same gene, which may influence other regions of the methylome. This suggests that, despite recent advancements in epigenetic research, future developments in AI technologies for detection, risk classification, and prognosis should take into account newly emerging multiple causal factors for epigenetic changes, such as environmental factors, and work on representative large DNA methylation datasets .Clinical prediction and treatment optimization play crucial roles in clinical diagnosis. Hematologic diseases are linked to the development of complications, putting the patient at risk for secondary conditions and eventually death. The use of AI as a potential tool to perform clinical prediction for hematologic disorders has been explored in recent years ,137. ForA study used ML algorithms to analyze clinical and genetic data from AML patients. The researchers developed a model that accurately predicted patient outcomes, outperforming other prediction models in terms of accuracy. The model also provided valuable insights into the underlying mechanisms of AML. Furthermore, ML could help to predict the in-hospital mortality of AML patients. A study on a cohort of 29,613 hospitalized AML patients used three ML models, including ML algorithm logistic regression, decision tree, and random forest. Among these, ML logistic regression and random forest models showed better performance in predicting the in-hospital mortality of AML patients (AUC: 0.78) . IdentifSimilar to this, a study used AI to create a clinical prediction model for CLL patients. The researchers developed an algorithmic population description approach (ALPODS) based on an XAI model that accurately predicted patient outcomes after analyzing MFC data from samples from more than 150 CLL patients. The ALPODS XAI algorithm model outperformed (AUC: 0.95) other prediction models in terms of accuracy . AI empoRisk identification and stratification are essential components in clinical diagnosis to predict patient outcomes and reduce therapeutic costs. AI could help to predict risks in chronic hematologic diseases, including thrombosis. Ryan et al. showed that ML methods help to reduce risks in hospitalized patients with deep vein thrombosis . A studyOn the other hand, a study by Shanbehzadeh et al. compared eight ML methods to predict the survival status of CML patients with either alive or deceased outcomes. Among the eight ML approaches, the SVM showed the best performance with an accuracy of 85.7%. This implies that AI could help predict long-term patient outcomes and choose the best treatment options .It is important to note that clinical prediction models require regular evaluation and optimization over time and in different settings, since the estimated condition may not always completely represent actual events in the future . AlthougAI in healthcare environments increases the accuracy of diagnosis, lowers overall healthcare costs, facilitates information sharing, and enhances target treatment, including the treatment of rare diseases. However, developing clinical AI systems presents a unique set of challenges that must be addressed to ensure their accuracy and reliability in clinical practice. One of the main challenges is to ensure the quality and availability of data. AI algorithms are trained on data, and if the data contain bias or are not representative of the patient population, the algorithm may produce inaccurate or biased results. Therefore, it is essential to ensure that the data used to train AI algorithms are of high quality, free of bias, and include variation among patient backgrounds and clinical conditions ,136. On Another challenge in developing clinical AI systems is to ensure that they are regularly evaluated for accuracy, sensitivity, and specificity. The evaluation process should be undertaken systematically and frequently to identify any issues and to ensure that the AI system remains accurate and reliable over time ,136. In Ethical issues are also a concern when developing clinical AI systems. One of the main ethical concerns is data privacy. AI systems process large amounts of personal and sensitive information, and if this information is not adequately protected, it can pose a significant threat to patient privacy and security. Strict regulations, such as the general data protection regulation, must be applied to ensure the security of health medical records, protect user data, ensure data integrity and traceability, and ensure the security of electronic signatures .Healthcare providers typically own medical records and patient-related information. Sharing clinical data should involve a degree of transparency in patient compliance. The best course of action would be to increase patient awareness of the types of data, the nature of the information in their records, and the recipients of those records. Patients ought to be not only told when their data are used in research but also informed of the findings and potential repercussions of that research. Jessica Morley et al. reviewed the narrations related to AI ethics and suggested that policymakers should consider and take action for AI ethics and help AI improve the quality of care .There is great potential and major concerns over privacy, confidentiality, and control of data in the era of AI about individuals once the data are shared. However, the benefits of data sharing should outweigh the drawbacks and be based on the interest of the larger community. AI applications in the diagnosis of hematologic neoplasms should comply with state- and region-based regulations. It would be in patients’ best interests to be actively involved in the development of policies on data sharing. Owners of AI-based technologies, however, should be aware of the potential risks involved with AI-based data management and processing, both in success stories and in instances when diagnosis went wrong. It also aims to pave the way for public policies to support a balanced agenda that safeguards personal information while enabling the use of data to improve clinical patient care and public health. Furthermore, healthcare policymakers at the global level need to create a unified legal and policy framework that supports a fair agenda that protects individual privacy rights, restricts commercial exploitation, and sends a strong public message while allowing the use of AI in healthcare datamining for both research and commercial purposes. Regulation should prioritize patient autonomy and consent and support ever-evolving data anonymization and protection techniques .The challenges in developing clinical AI systems extend beyond just ensuring the quality of data and regular evaluation of the AI algorithms. Another major challenge is to ensure the interoperability of different AI systems used across various clinical settings. The lack of interoperability can lead to fragmented care and inconsistencies in treatment recommendations . CurrentIt is also critical to make clinical AI systems user-friendly and accessible to healthcare professionals with various levels of technical specialization. User interfaces should be designed to facilitate easy navigation, and the output of the AI system should be presented in a clear and understandable format to aid decision making and change the future of healthcare services . In ordeIn conclusion, the application of AI in hematology diagnostics is on the rise, and it has the potential to greatly facilitate hematology diagnosis by combining results from different diagnostic methods. The use of AI in hematology diagnostics will help reduce the turnaround time, reduce diagnostic costs, and predict disease outcomes. However, it is important to note that AI cannot fully replace manual diagnosis due to its limitations, including limited databases, lack of validation and standardization, and the risk of systematic errors and bias. Furthermore, the use of AI poses data privacy issues; therefore, regulations on clinical AI systems, including evaluation of AI systems and regulations on ethical issues, are necessary to protect user information and privacy. To address the field’s current challenges, more research must be conducted, and AI should be incorporated into medical education.


Campylobacter gracilis, had the strongest association with childhood dental disease. This comprehensive model evaluation offers practical guidance for selection of appropriate methods for rigorous analyses of differential expression in metatranscriptomics. Selection of an optimal method increases the possibility of detecting true signals while minimizing the chance of claiming false ones.Understanding the function of the human microbiome is important but the development of statistical methods specifically for the microbial gene expression (i.e. metatranscriptomics) is in its infancy. Many currently employed differential expression analysis methods have been designed for different data types and have not been evaluated in metatranscriptomics settings. To address this gap, we undertook a comprehensive evaluation and benchmarking of 10 differential analysis methods for metatranscriptomics data. We used a combination of real and simulated data to evaluate performance  of the following methods: log-normal (LN), logistic-beta (LB), MAST, DESeq2, metagenomeSeq, ANCOM-BC, LEfSe, ALDEx2, Kruskal–Wallis and two-part Kruskal–Wallis. The simulation was informed by supragingival biofilm microbiome data from 300 preschool-age children enrolled in a study of childhood dental disease , whereas validations were sought in two additional datasets from the ECC study and an inflammatory bowel disease study. The LB test showed the highest sensitivity in both small and large samples and reasonably controlled type I error. Contrarily, MAST was hampered by inflated type I error. Upon application of the LN and LB tests in the ECC study, we found that genes C8PHV7 and C8PEV7, harbored by the lactate-producing  The human microbiome has emerged as an undeniable cornerstone for a multitude of health and disease outcomes . CompareAs with single organism bulk RNA sequencing and single-cell RNA sequencing (scRNAseq), one of the fundamental key analyses applied to metatranscriptomics data is differential expression (DE) analysis. DE analysis compares the expression level of taxa or microbial genes (or gene-families) between groups or conditions, e.g. different health/disease conditions, before and after treatment, or host developmental stages. It typically identifies genes whose expression levels are significantly associated with the outcomes of interest after controlling for nuisance factors such as batch or block information and host characteristics (e.g. demographics). Most currently available methods to analyze metatranscriptomic data rely on either the joint mapping of microbial DNA sequencing and RNA sequencing data  or analy‘gene-family’ as ‘gene’.There have been attempts to evaluate differential-abundance (DA) or DE methods at the species- or the taxon-level that have included many of the commonly used preprocessing and differential abundance analysis approaches , 25, 26.et al.  that provide good distributional results in the example data (Section S1).Distributional characteristics of the three aforementioned metatranscriptomics datasets are presented in the Supplement . In the The third dataset  is in a compositional form , and incFor all three datasets raw metagenomic (DNA) and metatranscriptomic (RNA) sequencing reads were submitted to Kraken2  for taxaIn Simulation I, multiple scenarios are defined by the following generative models with a nested factorial design. Three data generative model classes were used: zero-inflated log-normal (ZILN), zero-inflated negative binomial (ZINB) and zero-inflated gamma (ZIG) models. On top of each generative model, three factors are further considered: (1) baseline distribution, (2) disease effects and (3) batch effects (without interaction with disease), each of which is further described in later sections. The parameters representing the three factors are selected after consideration of the estimated parameters of the gene expression in ZOE2.0, as well as the other datasets.Sets of parameters were chosen for this simulation study so that they suitably represent distributions of parameter estimates in the example data. Once the data distribution is defined, we generate random samples to mimic a small  group, and the Benjamini–Hochberg procedure-based adjusted P-values, or ‘P-values are not available, we do not obtain its FDR; however, its type I error, sensitivity and accuracy are derived using the set of the genes declared significant instead of using the 5% cutoff. Because type I error, sensitivity and the proportion of the signal genes are sufficient to derive accuracy, and with a large proportion, it is mostly driven by type I error. In the following of Simulation I, we will mainly describe ZILN at the gene level based on ZOE2.0 and additional information is summarized at the end.Generative models Three generative models are considered: zero-inflated log-normal (ZILN), zero-inflated gamma (ZIG) and zero-inflated negative binomial (ZINB). We do not include the zero-inflated beta (ZIB) distribution, as only a few methods, such as the LB test, model relative gene expressions or abundances rather than their absolute quantities. Furthermore, because ZIB can be considered a compositional transformation of independent ZIG distribution, ZIG-based results should serve as a good proxy for ZIB-based simulations. ZILN is a mixture of log-normal distribution and zeros, where the proportion of zeros is parametrized by ng model  and as aBaseline parameters The ZILN parameters were estimated for the genes in the ZOE2.0 dataset for each of the disease and batch subgroups, using the method of moments  expressions for the disease group than for the healthy group, and 90% of the genes do not have any disease effects. Let The disease effect estimates for ZILN are estimated from the genes from the ZOE2.0 dataset assuming that there are no batch effects . The quaBatch effects The batch effects for the ZILN model are estimated for the genes in the ZOE2.0 and a random sample of these are presented in Other simulation scenarios Summaries of the estimated parameters for simulations in other generative models , those for the validation datasets  and those for the additional levels of measurement  are presented in For each of the three datasets—ZOE2.0, ZOE-pilot and IBD—we randomly sample a large number of genes (1000 or 10 000) among the prevalence-filtered  and ZOE-pilot (87.9%). These high zero proportions are comparable and actually higher than what is encountered in the corresponding metagenomics data (75% in ZOE2.0 and 68% in ZOE-pilot) . Specifiics data , as the Overdispersion, at the gene level and at the species level, was also examined when exploring the differences between metagenomics and metatranscriptomics data in the three datasets  under thThe effects of the above difference of choice of methods are reflected in the comprehensive simulation scenarios, which allow for different values of proportion of zeros We evaluate the goodness of fit of the three statically generative models  in the three real datasets with consideration of three transformation methods, to ensure a proper distribution was used for the parametric simulation. As another reference distribution and the one that the discrete part of the LB test is based on, Beta distribution is also included. For the continuous values , we applied the Lilliefors procedure , a data-This implies that the arcsine transformation is merely a location-shift transformation in the log-normal model for most of compositional values. LB showed slightly worse fit than ZILN. Meanwhile, the ZINB distribution has overall a reasonable fit to the RPK or the TPM transformed data, but has a poor fit to the arcsine transformed data . OverallWe compared the performance of the 10 methods using the ZILN-based simulated data for the null hypothesis of mean shift D1 in  or the \To connect with the difference between metagenomics and metatranscriptomics data, we find that modifying overdispersion has larger effects on FDR of ANCOM-BC2 and LB in simulations. ANCOM-BC2 with a large sample size has high FDR when overdispersion The rejection rates at 5% cutoff for the signal genes under alternative distributions, or the sensitivity of the tests, are illustrated in D2  and IBD (gut), to identify microbial genes whose expression is significantly associated with ECC or IBD, respectively.P-values are reported for illustration purposes  between ECC and non-ECC participants were evidenced by the high peak on the left side of the histogram of nominal P-values emanating from both LN and LB  level.LB and MGS have the highest sensitivity among others, with inflated type-1 error for sparse, small-sized data in LB and without the capacity of batch effects control for MGS. LN has reasonably good sensitivity and type I error control under various settings. Although it is the most comprehensive evaluation of DE analysis for the metatranscriptomics gene level, there are reasonable limitations of recommending DE methods due to the number of available datasets.Campylobacter gracilis, have the strongest association with childhood dental disease.Based on the LN and LB tests, genes C8PHV7 and C8PEV7, harbored by the lactate-producing We have focused primarily on statistical significance largely because statistical significance is more suitable to quantitative analysis, like DE analysis. The most significant microbial DE genes are expected to have enriched with functional activities. We have explored the biological interpretation of the DE genes in the real data application. However, we acknowledge interpretation of biological importance of the DE genes has a limitation in this paper.Microbiome_simulation_Appendix_bbad279Click here for additional data file.


Genotypes are strongly associated with disease phenotypes, particularly in brain disorders. However, the molecular and cellular mechanisms behind this association remain elusive. With emerging multimodal data for these mechanisms, machine learning methods can be applied for phenotype prediction at different scales, but due to the black-box nature of machine learning, integrating these modalities and interpreting biological mechanisms can be challenging. Additionally, the partial availability of these multimodal data presents a challenge in developing these predictive models.To address these challenges, we developed DeepGAMI, an interpretable neural network model to improve genotype–phenotype prediction from multimodal data. DeepGAMI leverages functional genomic information, such as eQTLs and gene regulation, to guide neural network connections. Additionally, it includes an auxiliary learning layer for cross-modal imputation allowing the imputation of latent features of missing modalities and thus predicting phenotypes from a single modality. Finally, DeepGAMI uses integrated gradient to prioritize multimodal features for various phenotypes.We applied DeepGAMI to several multimodal datasets including genotype and bulk and cell-type gene expression data in brain diseases, and gene expression and electrophysiology data of mouse neuronal cells. Using cross-validation and independent validation, DeepGAMI outperformed existing methods for classifying disease types, and cellular and clinical phenotypes, even using single modalities .We demonstrated that DeepGAMI improves phenotype prediction and prioritizes phenotypic features and networks in multiple multimodal datasets in complex brains and brain diseases. Also, it prioritized disease-associated variants, genes, and regulatory networks linked to different phenotypes, providing novel insights into the interpretation of gene regulatory mechanisms. DeepGAMI is open-source and available for general use.The online version contains supplementary material available at 10.1186/s13073-023-01248-6. The genotype–phenotype association has been found in many biological systems, such as brain-related diseases and behavioral traits. This association is very important as it will help us understand underlying cellular and molecular mechanisms like genes and pathways that causally affect the phenotypes , 2. ManyFor a mechanistic understanding from genotype to phenotype, several studies have shown that these variants influence disease risks by altering cell-type regulatory elements that affect the underlying gene expressions, which in-turn affect the disease phenotype , 17. ThiIt is also necessary to analyze genes and other regulatory elements that impact disease phenotypes. Several methods have attempted to associate genes with disease risks using gene expression profiles directly. For example, one study collected gene expression profiles from three publicly available AD datasets to predict the onset of AD disease using a variational autoencoder . DeepWASAs biological processes involve a complex interaction with multi-omics, emerging multimodal data enables studying such mechanisms at different scales. Several studies have attempted to integrate multi-omics data for brain disease predictions like SCZ –45 and AAnother challenge for genotype–phenotype studies is multimodal data integration. Due to several factors , multi-omics data will often be partially available for individuals , e.g., aAuxiliary learning is a type of learning technique aimed at improving the generalization of the primary task by learning secondary tasks along with the primary task –63. The In summary, genotype–phenotype predictions are very important in understanding molecular and cellular mechanisms, but existing genotype–phenotype methods have the following limitations: (I) Statistical methods such as Polygenic Risk Score (PRS) predict phenotypes directly from genotype. They are mostly linear models that cannot tackle genomic variants’ nonlinear effects and involve association studies that predict the correlation between genotype and phenotype but cannot explain how the inherited mutations are associated with the phenotype , 77. Morguided auxiliary learning for multimodal integration and imputation to improve phenotype prediction. DeepGAMI is a novel deep learning model that enables cross-modal imputation, predicts clinical phenotypes, and prioritizes phenotypic tissue- or cell-type functional genomics. Our contributions are three-fold. Firstly, DeepGAMI provides a biologically guided neural network framework for genotype–phenotype prediction using biology-guided dropconnect [https://github.com/daifengwanglab/DeepGAMI.To address these limitations, we propose DeepGAMI: Deep biologically pconnect . It intepconnect , genotyppconnect , and sinpconnect . We founDeepGAMI is a multi-view deep learning model that integrates multimodal data for predicting phenotypes  Random Forest classifier (RF), (2) Support Vector Machine SVM), (3) Fully connected neural network classifier (MLP), and four state-of-the-art methods: (4) Logistic Regression (LR) , (5) LasThe DeepGAMI model consists of four main layers.K samples and n TFs and is represented by K samples and l SNPs, The input layer consists of two data modalities, e.g., gene expression and SNP genotypes. Each row of the input matrix represents feature vector of a sample. For example, the gene expression matrix contains gene expression profiles of kth sample, kth column of XGEX and XSNP and represent gene expression and dosage data for kth sample, w1 and w2 are weight matrices with dimension Rl*p and Rn*p respectively, b1 and b2 are the bias vectors of length p, and meQTL ∈ Rl*p and mGRN ∈ Rn*p. The underlying idea of this layer is to model the regulatory relationships among genes such as TFs to genes and SNPs to genes. The input DropConnect is a regularization mechanism that sets random activation units to zero in each layer. It differs from dropout, which sets the random output units to zero while the former sets the connection weights to zero . For ourons Eqs.  and 4.3\Each data modality from the input layer goes through the biological DropConnect layer producing a set of output nodes of equal dimension  and testing (20%) sets and performed fivefold cross-validation (CV) on the training set for feature selection and identifying the optimal parameter combination. We then pick the best performing model based on the five-fold CV and evaluate the final performance on the test set. Training DeepGAMI model involves minimizing the overall loss function which is a combination of primary task (phenotype prediction) loss and secondary task  loss. The loss function used for the primary task is the cross-entropy loss   is a widIG provides feature importance scores where a higher score indicates higher importance of the feature. We first applied IG on the trained model  for generating feature importance scores for the input nodes  and latent space nodes (genes) for the test samples. This will help us identify important SNPs and TFs attributed to the phenotype’s outcomes. We then applied IG on the same model to extract link importance scores for the test samples. The link importance score gives us the importance score for the links between the input and the intermediate layer . This will provide potential clues in understanding the underlying relationships (SNPs to genes and TFs to genes) for the given phenotype. Using this link importance score, we can fine-tune prioritized regulatory networks for phenotypes.p-value < 0.05 were reported in the prioritized genes.From the prioritized functional links between SNPs and TFs to genes, we extract the genes having the most important links (top 10% of the link importance scores). We then perform enrichment analysis on these genes using Metascape . The enrt test (for binary phenotypes) and ANOVA (for multi-class phenotypes) for feature selection on the training set. As our intermediate layer consists of genes, we filter and keep the common genes between the two biological networks . We only keep selected features  present in these two networks. Next, we applied StandardScaler function from scikit-learn [The datasets vary between different cohort as they are extracted in different platforms using different protocols. We use the same preprocessing pipeline to process these inputs to the deep learning model and to reduce the effect of curse of dimensionality. We first use Student’s it-learn  for the t test for feature selection (keep significant SNPs and genes). We do not consider LD structure of the SNPs. We then include SNPs and genes which are present in eQTLs and GRN. Based on this pipeline (see Input Data Preprocessing), we ended up with 2080 SNPs, 126 TFs, and 84 intermediate layer genes as features.We used the population-level bulk RNA-seq and genotype data for the human dorsolateral prefrontal cortex (DLPFC) from PsychENCODE  for predWe also tested DeepGAMI on the genotype and cell-type specific gene expression data from the CommonMind Consortium imputed using bMIND  and a reWe used the bulk RNA-seq data in DLPFC and genotype data from the ROSMAP cohort  for our t-test instead. We then intersected the SNPs and genes with eQTLs and GRN. We ended up with 229 early-stage AD individuals and 275 late-stage AD individuals for the BRAAK score phenotype. For the COGDX phenotype, we had no CI (n = 166), mild CI (n = 130), and CI  individuals. Finally, for the CERAD phenotype, we ended up with no AD (n = 184), AD probable (n = 171), and AD definite (n = 149) individuals.For genotype data, we extracted SNP array (generated using Affymetrix GeneChip 6.0) dosage information for 1709 individuals. We extracted 146,763 eQTLs from GTEx consortium  for the n genes and K samples, and the electrophysiological features Patch-seq dataset includes transcriptomics and electrophysiological (ephys) data for 4435 neuronal cells in mouse visual cortex . We usedThe overall objective loss function for the model is computed using Eq. We first evaluated the performance of DeepGAMI using population-level genotype and bulk-tissue gene expression data of schizophrenia individuals in the dorsolateral prefrontal cortex (DLPFC). We utilized PsychENCODE consortium  data forWe performed fivefold cross-validation for selecting the optimal hyper parameters. Figure After selecting the optimal hyperparameters through fivefold cross-validation, we built a model using these settings on the training samples and evaluated its performance on the held-out test samples within the same CMC cohort. DeepGAMI  to prioritize SNPs, genes, and functional links on the held-out SCZ samples. Figure To demonstrate the application of DeepGAMI for predicting complex clinical phenotypes, we ran DeepGAMI on Alzheimer’s disease (AD) cohort, ROSMAP , which cWe split the data into training and held-out test sets using an 80:20 ratio. We used a fivefold CV on the training set for tuning the hyperparameters and obtaining the best performance. DeepGAMI outperforms state-of-the-art classifiers . These samples belonged to late-stage BRAAK individuals and CI (cognitive impairment in AD/Dementia). DeepGAMI was able to achieve AUC scores of 0.92 for late-stage BRAAK and 0.73 for CI  of the COGDX phenotype Fig.  and geneWe also tested DeepGAMI on additional non-omics modalities using an emerging Patch-seq dataset  containiTo evaluate DeepGAMI’s performance on Patch-seq data, we adopted the validation technique used in previous studies  and randWe compared the prediction accuracy of DeepGAMI with different methods using pairwise Kolmogorov–Smirnov test (k.s test) on the accuracy distribution over 100 runs. DeepGAMI has higher prediction accuracy for classifying cell cortical layers than other methods  neuronal cells patch-seq data  containiFollowing the prediction, we extracted the top 10% of the prioritized genes and importance scores of the 41 ephys features for each cell layer . Notably, we used genotype and cell-type-specific gene expression imputed using bMIND  from a rentclass1pt{minimaWe then performed enrichment analysis for the prioritized SNPs for each cell type. We found various known cell-type pathways and functions associated with SCZ Fig. B like orFigure One of the major contributions of this study is the use of prior biological knowledge to guide the neural network model for phenotype prediction. To test the effectiveness of these prior networks (GRNs and eQTLs), we performed an ablation study of DeepGAMI, where we compared our proposed model with two additional variants: 1) DeepGAMI Random—We generated random networks in place of prior biological networks and used these as the basis for DropConnect and (2) DeepGAMI Bernoulli—Based on the number of edges in the GRNs and eQTLs, we used Bernoulli distribution to generate networks with the same distribution as the original networks where existing edges has an 80% chance of being selected where other links have 20% chance of being selected. We compared the performance of these three variations on the bulk tissue SCZ cohort, inhibitory neuron cell type SCZ cohort, and AD cohort with CERAD score phenotype. We performed 5-fold cross-validation to assess the performance of all the variations. As shown in Additional file  DeepGAMIDeepGAMI is a novel interpretable deep learning model for improving genotype–phenotype prediction from multimodal data. Its auxiliary learning layer enables cross-modal imputation to predict phenotypes still when some modalities are unavailable. The model also takes prior biological information for aiding in prioritizing multimodal features  and feature networks  related to the phenotypes.As brain phenotypes involve complex cellular and molecular mechanisms, genotype and gene expression are a few of the many factors associated with mechanisms. We have demonstrated that DeepGAMI can handle various multimodal data as input in two scenarios. In the first scenario, DeepGAMI was able to accurately predict cortical layers using gene expression and electrophysiological features in mouse visual cortex Fig. . In the DeepGAMI uses fivefold cross-validation balanced accuracies and AUC scores to compare the performance of the model on various datasets. While cross-validation can be misleading when used for model selection, it can still be a useful technique for estimating the expected performance of the model on the test set and comparing results when the sample size is not large enough to split the data into separate train and test sets , 139. MoWe evaluated the scalability of DeepGAMI by varying the number of input features to the model and recording the runtime. For this analysis, we used PsychENCODE consortium  data forOne of the major contributions of DeepGAMI is its ability to make accurate phenotype predictions even when some modalities are missing, which is achieved using cross-modal imputation. DeepGAMI differs from traditional cross-modal imputation methods. Firstly, it aims to integrate multimodal data for improving phenotype prediction, rather than focusing solely on cross-modal imputation. Secondly, it can handle both lower sample size population-level datasets and single-cell multimodal datasets while the latter methods are mainly based on single-cell datasets. Lastly, existing cross-modal imputation techniques can be extended to perform various supervised and unsupervised learning tasks. However, it is important to note that this process involves building two separate models: one for modality estimation and another for prediction. As a result, there is a risk of missing phenotype-related shared features between modalities, which can potentially impact the accuracy of the predictions. Furthermore, traditional cross-modal imputation methods often do not allow for the incorporation of prior biological knowledge into the models. This can limit the interpretability of the results. DeepGAMI uses linear regression for cross-modal imputation. Even though our applications have shown the imputations work well, DeepGAMI can use nonlinear functions for cross-modality imputation, aiming to have nonlinear auxiliary learning. It is also possible to integrate existing cross-modal imputation methods into DeepGAMI’s auxiliary task of cross-modality imputation. This could potentially lead to further improvements in performance. The results from our analysis on AMP-AD data may be susceptible to batch effects and data-source-specific effects as we were able to only extract the ROSMAP cohort. Additional cohorts from the same study can further enhance the generalizability of our model.Disease variation is affected by both genetic and non-genetic factors which can include covariates like age, batch, sex, and ethnicity. In many cases, the covariate information is either missing or partially available. The genotype data can be adjusted by these covariates by the users. We test the effect of three covariates  on the CMC cohort. For each SNP, we regress out its genotype data across individuals by covariates and then input the residuals to DeepGAMI and the classification performance decreased  using three different methods: PLINK , LDpred2UC=0.61) . In futuIn this study, we presented DeepGAMI, an interpretable biology-guided deep learning framework for phenotype prediction using multi-modal data. We demonstrated that DeepGAMI improves prediction of disease types and clinical phenotypes and prioritizes phenotypic genomic features and regulatory networks in AD and SCZ, especially at the cell-type level. We envision DeepGAMI can be used to decipher functional genomics and gene regulation for other complex diseases.Additional file 1: Fig S1. Independent validation performance comparison on schizophrenia cohort with genotype and bulk tissue gene expression. Fig S2. Kolmogorov-smirnov (k.s.) test comparison of classification accuracy for Alzheimer’s disease cohort. Fig S3. Performance comparison of DeepGAMI with oversampling, without- oversampling, and binary classification on Patch-seq mouse visual cortex data. Fig S4. Integrated Gradient results for Patch-seq mouse visual cortex data. Fig S5. Independent validation performance comparison on schizophrenia cohort with genotype and celltype gene expression. Fig S6. Performance of DeepGAMI with its variations on ablation study across all classification tasks. Fig S7. Multiclass classification of AD phenotype  using methylation and gene expression data from ROSMAP cohort. Fig S8. Runtime comparison of DeepGAMI with MOGONET and Varmole on varying input feature sizes.Additional file 2: Table S1. List of all hyperparameters used in DeepGAMI. Table S2. Summary table of the total number of trainable parameters for each dataset. Table S3. Summary table showing features and class labels for different available phenotypes for ROSMAP AD dataset. Table S4. Balanced accuracy comparison for ROSMAP AD dataset. Table S5. Balanced accuracy comparison for Patch-seq dataset. Table S6. Summary table of the features for cell-type-specific Schizophrenia dataset. Table S7. Binary Classification results for cell-type-specific Schizophrenia dataset.Additional file 3. Prioritized bulk genes and SNPs for schizophrenia.Additional file 4. Prioritized bulk transcription factors to genes and SNPs to gene links for schizophrenia.Additional file 5. Prioritized genes and SNPs for cognitive impairment phenotype in Alzheimer’s disease.Additional file 6. Prioritized transcription factors to genes and SNPs to gene links for cognitive impairment phenotype in Alzheimer’s disease.Additional file 7. Prioritized genes and electrophysiological features for cell cortical layers in mouse visual cortex.Additional file 8. Prioritized cell-type genes and SNPs for schizophrenia.Additional file 9. Prioritized cell-type transcription factors to genes and SNPs to gene links for schizophrenia.