Knowledge of gestational age is critical for guiding preterm neonatal care. In the last decade, metabolic gestational dating approaches emerged in response to a global health need; because in most of the developing world, accurate antenatal gestational age estimates are not feasible. These methods initially developed in North America have now been externally validated in two studies in developing countries, however, require shipment of samples at sub-zero temperature.A subset of 330 pairs of heel prick dried blood spot samples were shipped on dry ice and in ambient temperature from field sites in Tanzania, Bangladesh and Pakistan to laboratory in Iowa (USA). We evaluated impact on recovery of analytes of shipment temperature, developed and evaluated models for predicting gestational age using a limited set of metabolic screening analytes after excluding 17 analytes that were impacted by shipment conditions of a total of 44 analytes.With the machine learning model using all the analytes, samples shipped in dry ice yielded a Root Mean Square Error (RMSE) of 1.19 weeks compared to 1.58 weeks for samples shipped in ambient temperature. Out of the 44 screening analytes, recovery of 17 analytes was significantly different between the two shipment methods and these were excluded from further machine learning model development. The final model, restricted to stable analytes provided a RMSE of 1.24  = 1.10-1.37) weeks for samples shipped on dry ice and RMSE of 1.28 (95% CI = 1.15-1.39) for samples shipped at ambient temperature. Analysis for discriminating preterm births , yielded an area under curve (AUC) of 0.76 (95% CI = 0.71-0.81) for samples shipped on dry ice and AUC of 0.73 (95% CI = 0.67-0.78) for samples shipped in ambient temperature.In this study, we demonstrate that machine learning algorithms developed using a sub-set of newborn screening analytes which are not sensitive to shipment at ambient temperature, can accurately provide estimates of gestational age comparable to those from published regression models from North America using all analytes. If validated in larger samples especially with more newborns <34 weeks, this technology could substantially facilitate implementation in LMICs. Preterm birth is a leading cause of neonatal morbidity and mortality worldwide, with more than 95% of its global burden being contributed by low- and middle-income countries (LMICs) . KnowledIn most LMICs settings where maternal access to ultrasound dating in early pregnancy is limited; measures such as last menstrual period, fundal height, or examination of the newborn are relied on for gestational age estimation -4. GestaAlgorithms developed in North American settings, deriving gestational age (GA) estimates through the biochemical analysis of newborn dried blood spots (DBS), have been shown to provide accurate estimates to within 2 weeks of best obstetric estimate . One of Many biomarkers used for newborn screening are susceptible to heat and humidity ,21. AcylTherefore, in a pilot study we investigated the difference in the recovery of the analytes in paired samples shipped at ambient temperature and on dry ice, from LMICs sites to IOWA (USA) for tandem mass spectrometric (TMS) analysis. Realizing the loss of recovery, our published external validations consequently used only DBS shipped on dry ice .We now use the pilot study data to report the impact on recovery by ambient temperature shipment. We also investigated models using machine learning with a restricted set of analytes, resistant to temperature effects. In this non-interventional, international validation study; we report changes in recovery of analytes and the accuracy of gestational age estimation comparing two shipment methods, and results from machine learning models using analytes resistant to temperature change, compared to those obtained by conventional methods using all analytes .This study was undertaken using data from the AMANHI, all children thrive (ACT), community based, prospective pregnancy and newborn cohorts from Pemba (Tanzania), Sylhet (Bangladesh) and Karachi (Pakistan). The rationale for these cohorts and associated biobank, procedures, and cohort characteristics have been described elsewhere . One of All study protocols for AMANHI cohorts were approved by ethical review committees of the World Health Organization (WHO) and appropriate institutional review board in each of the participating sites. Additionally, institutional/local sample utilization committees approved shipment of samples to Iowa for metabolic screening assay. Mothers were asked for additional informed consent before obtaining a heel prick from the baby.R 903, GE Healthcare, USA), within 24-72 hours of birth from newborns as per standard procedures. The DBS cards were air-dried and stored in air-tight zip-lock bags with desiccant at -80°C and shipped in dry ice to the State Hygienic Laboratory, Ankeny, Iowa, USA at regular intervals (ensuring processing before potency window). For the present analysis, a small subset of 330 cards were split into two, one of them was shipped in dry ice with other cards while the other set was shipped in ambient temperature  was used so that the means and standard deviation for the vector points remained consistent. To evaluate if the 330 selected samples and their simulated data were in fact an unbiased representation of the parent data set used in the validation exercise  + specificityf [c] – 1}P value were also performed using bootstrap methods [where c ranges over all possible criterion values. Graphically, J is the maximum vertical distance between the ROC curve and the diagonal line . Bootstr methods ,40. For Based on literature review (12 metabolites) and comparison of paired percentage change between the sample shipped on dry ice and that shipped in ambient temperature for every metabolite, a total of 17 metabolites  which decreased significantly with the samples that were shipped in ambient temperature  ; ambient temperature 1.28 weeks (95% CI = 1.15-1.39). A similar pattern was also observed in terms of MAE (1.09 vs 1.12)  the use of samples from a well-described cohort of infants with gestational age confirmed by first trimester ultrasound; b) availability of the parent external validity data from 1318 children in which our study of 330 paired samples was nested, enabling testing of the representativeness of the simulated training data set; c) masked, paired sampling design with results for each sample in the sub-set, available for both, shipment on dry ice and at ambient temperature enabling unbiased internal comparison in addition to comparison with parent study and published larger Iowa data set; d) availability of samples from both Asia and Africa with temperature, humidity and shipment conditions mimicking real life scenario; e) standard operating procedures (SOPs) for collection and storage of samples across sites enabling isolating impact of only shipment process. The primary limitation of this study is the small sample size. Although we tried to compensate by simulating a larger training data set and confirming simulated data set being representative of the parent data (Table S1 and Figure S1 in the Our findings are encouraging but need further investigation. This work provides early evidence that gestational dating models developed using metabolic data from analytes resistant to temperature and humidity effects from Asian and African setting perform as well as the model originally published by our collaborators using data from a United States-born cohort of 230 000 infants using all available analytes in the metabolic screen. Overall prediction of gestational age was better with an average difference between predicted and actual gestation age of 1.12 weeks in ambient temperature restricted analyte model compared to 1.5 in Iowa model . For difRecently validated regression methods and machine learning approaches, to predict gestational age based on newborn screening markers, provide reasonably accurate postnatal assessments of gestational age, in settings where first trimester ultrasounds are limited. We have built upon our existing postnatal gestational age prediction machine learning models to demonstrate the predictive potential of using a limited set of temperature resistant newborn metabolic screening analytes. The value of these findings is 2-fold, first these models provide the feasibility of using this approach in low- and middle-income countries where the cost of shipment of samples may impede utilization of the approach; and second the approach provides a possibility for further investigation of identifying a suitable set of analytes that may be amenable to investigation by more broadly available autoanalyzer’s than less commonly available tandem mass spectrometry. Validation of our model in a larger sample is also warranted to determine its broader external validity and investigate its potential or lack thereof for identification of very preterm births.


Traditional analysis of genomic data from bulk sequencing experiments seek to group and compare sample cohorts into biologically meaningful groups. To accomplish this task, large scale databases of patient-derived samples, like that of TCGA, have been established, giving the ability to interrogate multiple data modalities per tumor. We have developed a computational strategy employing multimodal integration paired with spectral clustering and modern dimension reduction techniques such as PHATE to provide a more robust method for cancer sub-type classification. Using this integrated approach, we have examined 514 Head and Neck Squamous Carcinoma (HNSC) tumor samples from TCGA across gene-expression, DNA-methylation, and microbiome data modalities. We show that these approaches, primarily developed for single-cell sequencing can be efficiently applied to bulk tumor sequencing data. Our multimodal analysis captures the dynamic heterogeneity, identifies new and refines subtypes of HNSC, and orders tumor samples along well-defined cellular trajectories. Collectively, these results showcase the inherent molecular complexity of tumors and offer insights into carcinogenesis and importance of targeted therapy. Computational techniques as highlighted in our study provide an organic and powerful approach to identify granular patterns in large and noisy datasets that may otherwise be overlooked. Significant efforts have been made over the years to better characterize and partition tumors into biologically and molecularly meaningful distinct subtypes. The rationale behind such tumor classification was to enable more precise, effective, and targeted therapeutic strategies. Such efforts have been rewarded, as in the case for breast cancer where death rates have dropped by 39% since 1989 . This isINK4A status for HPV+ Oropharyngeal cancer in the eighth edition of TNM guidelines  are clinically defined using the tumor-node-metastases (TNM) classification system and more recently the AJCC/UICC staging system, which rely heavily on pathological features such as primary tumor characteristics, lymph node spread, and distant metastasis, as well as relevant clinical history like smoking and alcohol usage , 6. Receidelines , 8 and Pidelines  represenidelines . TherefoData mining of the TCGA datasets, primarily those based on transcriptomic signatures has resulted in subtyping of carcinomas from various anatomical sites. In the case of HNSC, tumors have been classified into four primary groups, Atypical (HPV+), Classical, Mesenchymal, and Basal subtypes , 12. Howde novo integrated analysis of the bulk gene expression, methylation array, and microbiome datasets from TCGA Squamous Carcinoma patients. Specifically, we utilized multimodal spectral clustering followed by uniform manifold approximation and projection (UMAP), and potential of heat diffusion for affinity-based transition embedding (PHATE) dimension reduction  and epithelial markers  was subtracted from the expression sum of basal markers  to compute a basal composite score, a similar strategy to that of Salt et al. , level 3 TCGA data release. Samples with associated transcriptomic data were subset and 514 solid tumors were chosen for follow-up analysis. Probes associating with or targeting SNPs, as well as sex-chromosome associated probes were filtered from the analysis per Papillion-Cavanagh et al. Per-sample microbiome measurements produced by Poore et al. were downloaded from CBioPortal using the file labelled “data_microbiome.txt” . This daMethylation, transcriptomic, and microbiome data matrices were ordered by patient-sample ID, representing 514 head and neck solid tumors with complete multi-omic profiles. Each data modality was next introduced as an independent matrix to a self-adapting density-aware kernel developed by John et al. to produce a single sample-to-sample similarity matrix . This stUMAP dimension reduction analysis was performed using the R package uwot v0.1.11, using default parameters . PHATE dhttps://github.com/jebard/multimodal-tcga-hnsc. Clustering results and sample assignments can be accessed in CBioPortal as virtual study cohorts of the larger TCGA PanCancer HNSC 2019 set utilizing the following link: https://www.cbioportal.org/comparison/overlap?comparisonId=60804602e4b0242bd5d4984c.A graphical depiction of our analysis workflow for the panSCC and focused HNSC analysis is shown in Large datasets such as bulk RNA-seq of tumors in TCGA have been traditionally analyzed by hierarchical clustering, k-means clustering, and matrix factorization approaches like principal component analysis. These techniques, though powerful, have some inherent limitations due to the high-dimensional, often-sparse and noisy nature of such datasets, and thus may miss non-linear relationships hidden in the complex data , 28. SucFor our analysis, we first decided to test two dimension-reduction techniques, Uniform Manifold and Approximation and Projection (UMAP) and Potential of Heat-diffusion for Affinity-based Transition Embedding (PHATE). While UMAP was chosen due to its wide-spread adoption across single-cell analysis pipelines, PHATE was selected since it offers visualization that preserves the local and global structure of the data, denoises the data using heat-diffusion, and preserves sample-to-sample affinities when reduced to low dimensions .For both PHATE and UMAP based analysis, tumor samples across all cohorts were widely distributed , 34. ThiAlthough a large proportion of samples included in our analysis were primarily squamous in nature, a subset of tumors from bladder, cervical, and esophageal samples are adenocarcinomas. We next evaluated whether PHATE reduction preserved and separated tumors of varying cellular origins. We found that for both CESC and ESCA samples, adenocarcinoma tumors were tightly associated with other adenocarcinoma tumors, while CESC, ESCA, and HNSC squamous tumors were spread across the PHATE projection  characterized by high levels of 1, CYR61 –40. Thes1, CYR61 . Lastly,KRT14, as well as in the concerted downregulation of desmosome components including members of the plakophilin family PKP1 and PKP3, and desmosomal cadherin members desmoglein (DSG1 and DSG3), desmocollin (DSC2 AND DSC3), and junction plakoglobin JUP.  to 76 samples (EMT+) with a median sample-per-group count of 62 , 46–49. Using available clinical metadata, we first evaluated the HNSC HPV subtype, revealing that HPV+ tumor samples generated one predominant group  located at the left extreme of the PHATE1 axis . As expeTP63 transcriptional start sites were cluster specific, cg16764781 and cg06520450  score as previously described . The estALDH3A1, AKR1C1, and ARK1C3  cell populations in cutaneous SCC  state in HNSC tumors. Furthermore. our study reaffirmed the evolving notion that tumor ecosystems often exhibit a continuum of meta-stable, intermediary pEMT (or hybrid) states between the epithelial and mesenchymal poles , 73. SecCurrent treatment options for HNSC primarily consist of surgery, radiation and chemotherapy, administered in single or multi-modality regimens. However, these treatments still leave room for substantial improvement in efficacy and importantly does not consider molecularly defined subsets. Furthermore, approval of immunotherapy drugs such pembrolizumab and nivolumab for adjuvant treatment of recurrent and metastatic, or advanced unresectable HNSC, underscore the urgent need for research into tumor heterogeneity to better identify patients that are likely to be responders , 75. Givhttps://www.cbioportal.org/comparison/overlap?comparisonId=60804602e4b0242bd5d4984c.Publicly available datasets were analyzed in this study. This data can be found here: JB conceptualized the project, acquired and analyzed data, prepared the figures, and wrote the manuscript. NN provided funding and participated in the manuscript review and editing. MB and SS supervised the project, wrote and edited the manuscript, and provided funding. All authors contributed to the article and approved the submitted version.This project was supported by grants from the National Institutes of Health  and Community Foundation for Greater Buffalo to MB, SS, and NN.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.


Computational models have been successful in predicting drug sensitivity in cancer cell line data, creating an opportunity to guide precision medicine. However, translating these models to tumors remains challenging. We propose a new transfer learning workflow that transfers drug sensitivity predicting models from large-scale cancer cell lines to both tumors and patient derived xenografts based on molecular pathways derived from genomic features. We further compute feature importance to identify pathways most important to drug response prediction. We obtained good performance on tumors (AUROC = 0.77) and patient derived xenografts from triple negative breast cancers (RMSE = 0.11). Using feature importance, we highlight the association between ER-Golgi trafficking pathway in everolimus sensitivity within breast cancer patients and the role of class II histone deacetylases and interlukine-12 in response to drugs for triple-negative breast cancer. Pathway information support transfer of drug response prediction models from cell lines to tumors and can provide biological interpretation underlying the predictions, serving as a steppingstone towards usage in clinical setting. Facing these shortcomings, there has been a shift towards tailoring drug response profiling using in-vivo tumors or patient-derived xenografts (PDX)1. However, the process of deriving PDX remain costly and time consuming, which hinders its application into clinical practice. Thus, a computational framework that would leverage the plethora of knowledge and data already collected on cancer cell lines to bridge the gap between cell lines and in-vivo tumors or PDX data may be useful in addressing the cost and time limitations of PDX.Cancer cell lines have been designed to serve as in-vitro research models to facilitate high-throughput screening of cancer drugs. However, cancer cell lines have been shown to be poor predictors of human responses owing to factors such as underrepresented tumor heterogeneity and lacking components of the tumor microenvironmentUsing data from cancer cell lines has the potential to enrich the currently scarce tumor data. However, there are three additional challenges facing computational methods that aim to translate models developed on cell lines to tumor data: mapping genomics data from in-vitro to in-vivo measured data, mapping outcome measures and explaining the predictions.2. However, estimation of gene expression level may vary between platforms or between batches, leading to poor reproducibility3. In order to address this limitation, Geeleher et al4 introduced a robust statistical correction methodology, showing that cell line model can guide the prediction of clinical drug sensitivity when they have similar distributions, with Gruner et al5 successfully applying their regression model to cancer sub-types of Triple- Negative Breast Cancer (TNBC) patient tumors. Additionally, Turki, Wei, and Wang6 augmented the cell line training data by including the best matching tumor gene expression subsets through the Procrustes analysis.The first challenge involves the differences in collected pre-treatment genomic data. Pre-treatment or baseline gene expression data are effective biomarkers for predicting drug response of cell lines, compared to gene-level mutation data, copy number variation data7 first selected the most similar cell lines for each patient and then prioritized therapeutic agents based on the AUC on the most similar cell lines. Another approach by Sharifi-Noghabi et al8 used an ad-hoc threshold to binarize IC50 of cell lines and trained a classifier incorporating embedded multi-omics data to classify clinical outcomes.A second challenge is the mapping of aggregates extracted from drug response curves in cell lines, such as IC50 or Area under the drug response curve (AUC) to dichotomous values required in making treatment decisions in patients, such as responsive/non-responsive. In order to address this challenge, a decision model developed by Cheng et al9.The final challenge involves model explainability, which is important to achieve better clinical utility10 and by identifying pathways with large effect on the prediction, we increase the explainability of the models, providing mechanistic interpretation of the sensitivity or resistance of a drug on a certain tumor.In this study, we describe a prediction model that addressed these three challenges. We leverage large-scale cancer cell line data and biological knowledge in the form of molecular pathways to transfer models trained on cell lines in order to predict outcomes in in-vivo tumor and ex-vivo patient-derived xenograft (PDX) data. We address the transferability of pre-treatment transcriptomics by mapping them to molecular pathways, which has proved robust in our previous publication Tang and Gottlieb6, tissues11 or data sources12. In addition, we integrated both genomic and chemical properties of drugs along with pre-treatment gene expression to enable pan-cancer, pan-drug predictions, which has been shown to be beneficial in improving generalizability comparing to previous single-drug models13.The use of large-scale cancer cell line data through transfer learning enable us to address the limited availability of tumor data and has proven beneficial for transferring different modalitiesOur transfer learning framework enables us to provide predictions both to dichotomous outcomes (responsive or not) and to continuous outcomes (AUC on PDX data), thus addressing the second challenge, involving translating response curves to more clinically-meaningful outcomes. In order to demonstrate this, we validated our transfer learning model in three independent scenarios: in in-vivo tumors data from clinical trials and in ex-vivo PDX data with both dichotomous and continuous outcomes. To conclude, we demonstrated that knowledge of in-vitro cell line drug response can be beneficial for inferring drug sensitivity in tumors and that biologically informed computational model can predict drug sensitivity and provide system biology insights about drug actions.We considered clinically relevant data across two axes. The first axis is the data type, including human in-vivo tumor drug screen data and ex-vivo PDX data. The second axis is whether the response was measured as dichotomous (responsive/non-responsive) or continuous , which resulted in three scenarios across the two axes: tumor data, consisting of 24 drugs and 16 cancer types, PDX with dichotomous outcomes (PDX-D) consisting of 22 drugs and six cancer types measured in PDX models, and PDX with continuous outcomes (PDX-C) which is a screening of 340 drugs in triple-negative breast cancer (TNBC) measured in PDX models . In our previous study, we demonstrated high prediction performance of our model on cancer cell line data, outperforming previous methodsp value < 0.002), but for PDX-D, the un-transferred model obtained slightly better results. For the predictions of PDX-C, there was minor difference in performance between using only PID, only REACTOME or both, with slightly better performance when using the PID pathway. Here too, transferred models were better than non-transferred models . As such, we used the best performing model of each dataset for further analysis . The best performance in a five-fold cross validation was achieved by incorporating both PID and REACTOME pathways , has a reported role in the mTOR function in both immune cells and tumor cells through the pairing of chemokine to its receptor20. Notably, chemokine CXCL12, one of the components in the predicted pathway, is a mTOR-associated chemokine in breast cancer20. It has been further demonstrated that there is a positive feedback loop between mTOR activation and the binding of CXCL12 to its receptor CXCR4—inhibiting the mTOR activity may prevent cancer metastasis because it subsequently decreases CXCL12 interaction.We highlight one example from our prediction results: our model obtained AUROC of 0.9 ± 0.001 for predicting sensitivity to everolimus, an immunosuppressive drug of type mTOR inhibitor, screened for breast cancer patients in our data. Our top eight predictive pathways  display significantly different level of enrichment between responders and non-responders to everolimus  is the key linking to resistant response. This pathway has a positive control on the expression of host mTOR21. Highly active the pathway signals, higher expression levels of mTOR proteins. Our result showed that non-responder samples had downregulated expression in the pathway, indicating there is a low expression level of mTOR signals in non-responder samples  mediated anti-inflammatory cytokines production24 and IL10 synthesis that plays a role in immune escape25. Together, our finding supports the current knowledge that cellular activity in the mTOR signaling pathway might affect the drug sensitivity in tumors.In addition, our finding showed that a specific anti-inflammatory response . In this case, the best AUROC (0.63 ± 0.11) was obtained when integrating the PID pathways only  to 0.68 ± 0.2 (with transfer learning), while for drugs not in GDSC improved from 0.60 ± 0.2 to 0.63 ± 0.1. Similarly, the improvement in the cancer types appearing in GDSC improved from 0.66 ± 0.05 to 0.68 ± 0.04 and those not appearing in GDSC improved from 0.49 ± 0.07 to 0.56 ± 0.01 AUROC Fig. .13, including paclitaxel, gemcitabine and erlotinib. Our pathway-based transfer learning model achieved better performance on paclitaxel and erlotinib with 0.8 ± 0.1 and 0.67 ± 0.16 AUROC, respectively but achieved lower performance on gemcitabine than Geeleher et al4 . Our transfer learning approach achieved good performance using both drug AUC measures, but the model using the adjusted AUC values displayed higher benefit from transfer learning  and PID’s IL_12_2PATHWAY . Both pathways play a role in the immune response. Histone deacetylases (HDACs) regulates enzyme activity responsible for inflammatory development while interleukin 12 (IL-12) is responsible for balancing autoimmune attack and immune response against foreign antigens27. Class II HDAC inhibitors have shown efficiency in reducing cell proliferation in breast tumors, both in-vivo and in-vitro28. Indeed, a previous study showed improved immune responses in TNBC patients treated with combination of HDAC inhibitor and immune checkpoint inhibitor29. IL-12 also has well-documented in-vivo and in-vitro antitumor activity30. Through promoting antitumor immunity that activates natural killer (NK) and T cells, IL-12 demonstrates potential potency in combination with immunotherapy. For example, TNBC patients that were unresponsive became sensitive to anti-PD-L1 immunotherapy after injecting IL-1231. The combination of HDAC inhibitor and immunocytokine NHS-rmlL12 improved protective immunity in breast tumors that have inherited immune deficiency32.We exemplify a PDX from a specific PDX with high correspondence between the observed drug sensitivity and the model predicted one. Our model obtained a high correlation between predicted drug response and actual response values with R137 Fig. . Our modWe note that the remaining eight out of top ten pathways for PIM137 across all tested drugs were features derived from the molecular fingerprints of the drug structure.We have developed a new transfer learning model for predicting drug sensitivity in pre-treatment in-vivo and ex-vivo tumor data based on mapping of pre-treatment genomic data to molecular pathways. Our approach utilized a model pre-trained on large-scale cancer cell line drug screening data and incorporates both genomic and chemical features. We demonstrated that our pathway-based method achieved good performance in different scenarios, including in-vivo tumor data and ex-vivo PDX data in a cross-validation and had better, or comparable performance to previous expression-based models for specific drugs. Our approach proved especially robust when dealing with tumor data from different sources and using different measures to determine drug sensitivity.The proposed approach supports a flexible model architecture that is adaptable to data representation from one task to other related tasks, enabling information extraction directly from cell line response without continuous-discrete conversion. We showed that in general, knowledge transferred from cell lines improves prediction accuracy in tumor or PDX drug sensitivity data. The only exception was in the dichotomous PDX data, where transfer learning did not manage to improve the results. A possible reason is that the PDX-D data shared the least number of drugs and cell types with the GDSC cell line data. Only 3/12 drugs and 2/5 cell lines in the PDX-D data exist also in GDSC Table . In contOne strength of our approach is the explainability of our model via the functional roles of molecular pathways. Pathway-based features can reflect molecular interactions between genes active in tumors and the downstream effect of drug-targets, as we exemplify for everolimus.2 supports a practical direction towards clinical application. However, there might be cases where other types of genomics data help obtain a significant performance of the models. Two such examples are responses to gemcitabine and erlotinib. Since our method is capable of incorporating other types of genomic data, such as mutation and copy number variation data, we demonstrate that we are able to improve significantly our performance on erlotinib through the incorporation of these types of genomics data. However, we failed to improve upon previous methods for gemcitabine, either with only transcriptomic data or with multi-omics approach. One possible explanation is the existence of specific mutations in TP53 that were shown to be predictive of gemcitabine sensitivity in a clinical trial33 and might have not have been as well represented in our pathways as in methods relying on individual genes. Finally, in this study, we focused on monotherapy prediction and excluded drug combination data. While we previously presented a pathway-based deep neural networks to predict in-vitro drug combination34, the large space of drug combination and the relative scarcity of in-vivo drug combination screens limit the transferability of our models in this scenario.There are three major limitations of our method. First, the use of Shapley values and statistical significance infers associations, not causal relation. Experimental validation is thus needed in order to further identify the possible roles the top predictive pathways play in drug sensitivity/resistance. Second, we have used only transcriptomic data in our study, owing to lack of other genomic data from the studies we assembled. This enabled our models to transfer learn predictions for hundreds of drugs across multiple types of cancer, which has previously limited methods relying on multi-omics data. Thus, the higher prevalence of transcriptomic data in clinical cancer studies together with the observation that transcriptomic data has been proven to be the best predictor of drug responseA noticeable trait of our method is that performance was largely dependent on sufficient sample size, such that our method tended to perform much better on drugs and cancer types that had relatively large sample size. This is evident from the fact that the overall AUROC is much higher than the average on the drugs/cancer types as the overall AUROC is influenced more from drugs/cancer types with large sample size while averaging on the individual performance of each drug/cancer type assigns equal weight to drugs/cancer-types regardless of sample size. Similarly, we obtained relatively lower performance on the PDX-D dataset, owing in part to the low number of the responders within the dataset. It is thus imperative to continue to conduct more experiments on underrepresented drugs/cancer-types in order to improve the prediction power of computational methods like the one we presented.We demonstrate that through a pathway-based transfer learning model, we are able to transfer information from large compendia of cancer cell line data to in-vivo and ex-vivo data. This is yet another steppingstone in the move from research to clinical application, where our model may be potentially used as a pre-screening tool for patients.35. We filtered cell lines that had no gene expression profile or no measured drug response value. Human tumor drug screening datasets were collected from literatures39 and GEO search using the same keyword searching strategy that was described Lee et al. paper39 with the following search terms: “pre-treatment OR baseline transcriptome AND cancer patient survival”, “pre-treatment gene expression AND cancer drug response” performed in November 2021. We considered only tumor samples that had available pre-treatment gene expression and drug response and patients that have been treated with only one drug during the treatment period in order to avoid combinatorial effect. Most in-vivo studies use the Response Evaluation Criteria in Solid Tumors (RECIST) criteria to categorize clinical drug sensitivity , while some measures were based on the time from treatment to disease progression, such as Progression Free Survival (PFS), Recurrent Free Survival (RFS) and Time to Progression (TTP) (Table 36 (dichotomous response) and for primary treatment-naïve triple-negative breast cancer from Powell et al38 (AUC measured response). Notably, the Powell et al. dataset was aimed at drug repurposing and thus tested a panel of FDA approved agents, including drugs not intended for cancer, with the hope of finding new drug mechanism of action and shorten the translational time line. We downloaded PID14 and REACTOME15 pathway gene sets from MSigDB41.In-vitro human cell lines screen was obtained from the Genomics of Drug Sensitivity in Cancer (GDSC)P) Table . Ex-vivoAll data used in this study were downloaded from publicly available repositories and require no additional institutional ethical approval.42. In short, each dataset was treated as a batch, and then was standardized gene-wise to have a mean of zero and standard deviation of one. Random effects across batches were then estimated via the Bayesian method developed by Johnson et al42. Third, scaled data was adjusted based on the estimators obtained from the previous step. All three steps were completed by using the pyCombat package.Harmonization: To make expression values generated from different platforms comparable Table , all dat15. We ran permutation tests 1000 times using the python library GSEApy to establish significance scores. We termed the resulting pathway features as EXP.Pathway enrichment analysis: We conducted single-sample Gene Set Enrichment (ssGSEA) analysis on the harmonized gene expression data to quantify pathway activity in each sample using the PID and REACTOME pathways43 with PID and REACTOME pathways15 to quantify the propagated effects of therapeutic targets on pathway genes within the protein–protein interaction network which was downloaded from the STRING database44. Detailed procedures for both feature types were described in our previous work10 (DGNet features).Drug features were represented by canonical SMILES and drug targets, respectively. We used one-hot vectors with a fixed size of 256 bits of Morgan fingerprint to encode chemical structure for each drug (CHEM features). For drug target data, we performed the Network-based Pathway Enrichment Analysis (NetPEA)10. Other hyperparameters were also adopted, including learning rate of 0.0004, early stopping at 30 patience , ELU activation function for each hidden layer except for the output layer, and the weight values after a gradient update were clipped between −5 and 5. The loss function is RMSE, and the optimization function is Adamax. We performed five-fold cross validation and then used the best model to refit the whole dataset to obtain a pre-trained cell line model. The drug sensitivity data in the GDSC dataset were quantified using two continuous variables, IC50 and AUC, representing half maximal inhibitory concentration and the area under the fitted dose response curve, respectively. We converted IC50 to -log10IC50 and use 1-AUC to define drug sensitive as the AUC values ranges from 0 to 145. We eventually used only the model that predicts 1-AUC as the final pre-trained model because it had better performance in terms of Root Mean Squared Error (RMSE) on the GDSC data, with an average RMSE of 0.096 across five test folds, comparing to mean RMSE of 0.42 achieved by the model that used − log10IC50 as the response variable.After data preprocessing described above, input features consisting of CHEM, DGNet and EXP were concatenated and fed into a deep neural network with four hidden layers with 1000, 800, 500, and 100 neurons respectively. Dropout was applied to the neuron level with 10% probability to drop as described in previous studiesWe considered different parameter settings for the pretrained model, including the number of pre-trained layers to transfer and whether to retrain the pretrained parameters or no retraining at all. We also investigated several hyperparameter spaces, including the choices of optimizer, activation function, batch size, dropout rate, learning rate, the number of neurons for the newly added layers, and the number of new hidden layers. The range of parameter values explored is detailed in Table tumor/PDX datasets were converted into pathway-level features following the procedure described above. Drug features were obtained using the same processing procedures. Features from the pre-trained cell line in-vitro model were used to fit a deep classifier  and continuous AUC.For the dichotomous outcome (PDX-D), we designed a classification task that used the same workflow described for the tumor transfer learning model, but due to the large class imbalance between responders and non-responders, we randomly subsampled from the non-responder class, so that every drug has a ratio of 1:3 for responders and non-responders. This ratio achieved the best performance relative to compared ratios of 1:2, 1:3 and 1:4, achieving mean AUROC 0.58, 0.61, 0.58, respectively.ftp://ftp.sanger.ac.uk/pub/project/cancerrxgene/releases/current_release/GDSC2_public_raw_data_25Feb20.csv) and applied the same statistical workflow as previously described38.For the PDX-C regression task, we used mean squared error (MSE) as the loss function, and no activation function was used in the output layer to restrict output range. Root mean squared error (RMSE), averaged over five folds, was used to evaluate model performance. Other procedures such as expression data harmonization, feature engineering, pretrained model construction, transfer learning model tuning, and validation were also performed following the same practice as detailed for tumors. This dataset used a customized AUC measures which has an inverse relationship with the dose-dependent AUC of GDSC Fig. . TherefoWe compared prediction performance with/without using the transfer learning method by averaging AUROC scores for dichotomous predictions, and RMSE values for continuous outcomes. For within-sample comparison, we further broke down samples into GDSC drugs/GDSC cancer types and non-GDSC drugs/non-GDSC cancer types. We focused on drugs/cancer types that have at least five samples to calculate performance scores on a single drug/single cancer type basis. Finally, we averaged scores across GDSC samples, and non-GDSC samples, respectively for comparison.46 via the DeepExplainer function of the SHAP47 package. Feature importance is quantified by the mean of absolute Shapley values across samples. The higher the value, the greater contribution to the prediction.We estimated the contribution of input features to the prediction values based on Shapley valuesFor dichotomous predictions, we used true positives and negatives to define responders and non-responders, respectively as they are accurately labeled by our models. After prediction categorization, we identified top contributing pathways for two groups of samples, respectively.p values.For continuous predictions, we ordered features by ranked importance across all samples. Enrichment of each pathway was compared for two groups using Mann–Whitney U test, and the corresponding two-sided p values were adjusted with Benjamin-Hochberg false discovery rate method. We set the significance level at 0.05 for the FDR-adjusted Results of repeated cross validation are presented in the format of mean ± SD. Statistical tests, significance, and the number of samples are presented followed by the respective data or figure legends.Supplementary Information 1.Supplementary Information 2.Supplementary Information 3.


Schizophrenia is a widespread mental disorder that leads to significant functional impairments and premature death. The state of the art indicates gaps in the understanding and diagnosis of this disease, but also the need for personalized and precise approaches to patients through customized medical treatment and reliable monitoring of treatment response. In order to fulfill existing gaps, the establishment of a universal set of disorder biomarkers is a necessary step. Metabolomic investigations of serum samples of Serbian patients with schizophrenia (51) and healthy controls (39), based on NMR analyses associated with chemometrics, led to the identification of 26 metabolites/biomarkers for this disorder. Principal component analysis (PCA) and orthogonal partial least squares discriminant analysis (OPLS-DA) models with prediction accuracies of 0.9718 and higher were accomplished during chemometric analysis. The established biomarker set includes aspartate/aspartic acid, lysine, 2-hydroxybutyric acid, and acylglycerols, which are identified for the first time in schizophrenia serum samples by NMR experiments. The other 22 identified metabolites in the Serbian samples are in accordance with the previously established NMR-based serum biomarker sets of Brazilian and/or Chinese patient samples. Thirteen metabolites  that are common for three different ethnic and geographic origins  could be a good start point for the setup of a universal NMR serum biomarker set for schizophrenia. Schizophrenia is a widespread mental illness ranked in the top 25 causes of disability worldwide , which lThe state of the art in this research area indicates that this mental illness is the result of complex interactions between genetic and environmental factors, and the underlying pathophysiology is not completely understood. The current diagnostic criteria for psychiatric diagnosis are based on clinical phenomenology, and they are limited to psychiatrist judgment after a standard clinical interview and reports from patients or caretakers. Diagnosis is significantly hampered in cases with advanced mental disorders due to difficult communication with the patient and/or lack of credible information from the environment. Besides gaps in the understanding and diagnosis of the illness, there is also a need for personalized and precise approaches to patients through customized medical treatment and reliable monitoring of treatment response . In ordeNMR spectroscopy and MS spectrometry are the two most-used platforms in metabolomics. Compared with MS analyses, NMR is less sensitive and has limited resolution. However, NMR analyses offer high reproducibility and quantitative accuracy using intact biospecimens without the need for separation . PreviouIn accordance with the objective of the “Mental Health Action Plan 2013–2030” created by the WHO for strengthened information systems, evidence, and research , and in This study included 51 patients with schizophrenia and 39 healthy individuals in the control group, and the two groups of the investigated individuals were carefully paired regarding sex and age. Schizophrenia patients underwent the same treatment with the antipsychotics regarding dose and time, illness stage, and symptoms before hospitalization. NMR analyses of blood samples were performed in triplicate.1H-NMR data sets were processed applying Bruker Topspin software, and the spectra phases and baselines were corrected using automatic options. The 0th order phase correction was carried out manually, contributing to noise variance removal . When the number of variables is 32 K, as in this case, the simple univariate methods are not easily applicable. Nevertheless, univariate statistics such as skewness and kurtosis could be informative to some extent and helpful in determining the method of scaling or variables regions with significant discrepancy from a normal distribution or even potential outliers. Kurtosis for a normally distributed data set should be near 3. Pronounced positive values for both statistics were observed in the region of spectra between 3.2 to 3.9 ppm as well as at 1.21 ppm. Therefore, samples that contributed to this area of spectra are considered possible outliers. Spectral regions below 0 ppm and above 8 ppm, without the presence of any significant resonance signals, were also removed from further consideration. After careful analysis of the statistics, 6 samples  were identified as potential outliers . AdditioIn order to obtain the most reliable PCA models, different centering and scaling methods were used: Pareto centering and scaling, autoscaling, and class centroid centering and scaling. The resulting number of PC components of a PCA model was determined using RMSECV from 7-fold cross-validation . The PCA model using Pareto scaling with mean centering data accounted for a total variance of 90.95%, and the first two components provided a very good separation between the two main classes . Other rThe most positive contribution to the PC2 loading graph b, corresPattern recognition data analysis was carried out in two steps. In the first stage, all the variables were mean-centered (or class centroid centered); thereafter, they were autoscaled by dividing each variable by its standard deviation (or pooled standard deviation) and then analyzed using a supervised pattern recognition method—orthogonal projection on latent structure (O-PLS), which was developed by Trygg et al. . The O-PPrediction capabilities were tested for the chosen number of components with independent test data set comprising 32 samples of ‘Schizophrenia’ and 39 samples belonging to the ‘Control’ class, a total of 71 samples. As a result, the final number of components was selected according to the minimum value of the root-mean-square error of prediction (RMSEP) obtained for a different number of model components. Predictions for both classes using autoscaling as a preprocessing method for centering and scaling are presented in The corresponding confusion matrix for the classification of the external test dataset according to the PLS-DA model with autoscaling preprocessing is shown in The score plot of the first predictive LV 1 component (comprising 26.83% of the variance) to the first orthogonal LV 2 component (comprising 28.39% of the variance) is shown in When the number of samples in the analyzed groups is unequal or unbalanced, then using a standard approach in data pretreatment for PLS-DA analysis will not usually result in the most appropriate class separation boundary . In suchThere are many ways to select discriminating variables with diagnostic values, such as variables with large regression coefficients , normaliIn accordance with the results of chemometric analyses, the identification of metabolites as potential biomarkers in blood samples of patients with schizophrenia from a Serbian cohort was performed based on analyses of spectral 2D NMR data obtained in TOCSY, 2DJ, and HSQC experiments. TOCSY spectral data  led to tMetabolomic analyses of serum samples of Serbian patients with schizophrenia and healthy volunteers as a control group led to the identification of 26 metabolites as a biomarker set for this psychiatric illness. Aspartate/aspartic acid, lysine, 2-hydroxybutyric acid, and acylglycerols were identified for the first time in the patients’ serum samples as schizophrenia biomarkers based on NMR-metabonomics. Changes in these biomarkers could be a consequence of bioenergetic abnormalities in schizophrenia patients. The lack of energy is now recognized ,11, metaIt is interesting that fifteen of the twenty standard amino acids that are commonly found in proteins have been identified as biomarkers in schizophrenia patients’ serum samples. Previously detected amino acid biomarkers such as alanine and glutamate may suggest the possible disturbed use of glucogenic amino acids via degradation and deamination processes . The carThe other 22 biomarkers were previously identified in serum samples of patients from Brazil and China ,13,14,15Established NMR-based serum biomarker sets of Serbian, Brazilian, and Chinese schizophrenia patients overlap in 13 metabolites. Comparing just Serbian and Brazilian results, the biomarker sets show an overlap of 18 metabolites, while Serbian and Chinese sets overlap with 17 biomarkers, and Brazilian and Chinese biomarker sets have 15 mutual metabolites. On the other hand, each of these NMR biomarker sets contains metabolites that have not been identified in the serum samples of patients of different geographic and ethnic origins. Of course, the differences in the offered NMR biomarker sets could be mainly caused due to lack of consistency in the strategy and methodology in the analysis of samples with different origins. Nevertheless, thirteen metabolites , identified by various NMR experiments and different instruments in all three biomarker sets and in the serum samples from different origins, could be a good starting point for further efforts in order to establish a universal NMR serum biomarker set for schizophrenia.2O .Sampling was performed in compliance with the ethics committee approval of the Special Hospital for Psychiatric Diseases “Kovin”, University of Belgrade—Faculty of Chemistry and Blood Transfusion Institute of Serbia. Blood samples of selected medically treated patients with schizophrenia were provided from the Special Hospital for Psychiatric Diseases “Kovin,” and corresponding samples of healthy controls were provided from the Blood Transfusion Institute. In compliance with ethics committee approval, patients or their caretakers and healthy volunteers signed written consent for the donation of their blood samples for this research. Through this research, 51 blood samples of male (25) and female (26) patients, 32 to 68 years old, were analyzed. A total of 15 patients were using antipsychotics of the first generation , 20 patients were using antipsychotics of the second generation , 7 patients were using antipsychotics of the first generation and second generation and 9 patients were using anxiolytics . The control group consisted of 39 healthy volunteers, males (27) and females (12), 23 to 60 years old. Sample preparation counted on three independent blood samples. After sample collection, blood was kept on ice for one hour and centrifuged. Obtained serums were stored at −80 °C. Prior to NMR analyses, serum samples were diluted with D1H-NMR spectral data were performed by PLS Toolbox version 8.9.1 [1H-NMR spectra into MATLAB workspace was accomplished by General NMR Analysis Toolbox (GNAT) version 1.2 [1H-NMR spectra was implemented through Interval Correlation Optimized shifting (icoshift) version 3.0 beta [All data processing in this work was applied using toolboxes and software implementations, including in-house developed scripts/codes conducted under MATLAB version 9.7  . Preprocon 8.9.1 . Readingsion 1.2 , and in sion 1.2 . Alignme3.0 beta .1H-NMR spectral dataset was set, preserving the original spectral processing parameters predefined by the Bruker Topspin software. We have predefined two main classes assigned as ‘Schizophrenia’ and ‘Control’ relating to schizophrenia patients and the healthy control group of samples. Using the ascribed methodology, two separate datasets were assembled containing 265 samples (149 of class ‘Schizophrenia’ and 116 of class ‘Control’) gathered in triplicates of the samples, one containing already processed spectra under Bruker Topspin software and another one with reprocessed NMR spectra under GNAT. All other data related to the class and label assignments for the respective samples remained the same in both data sets. Further data pretreatment and modeling were performed in the same way for both data sets. In this way, the potential contribution from random errors should be possible to discriminate. In addition, an independent test data set comprising 32 samples of ‘Schizophrenia’ and 39 samples belonging to the ‘Control’ class, for a total of 71 samples, was also assembled for the purposes of external validation of OPLS-DA models. As in the case of the previous two data sets, the same data pretreatment, including preprocessing, was performed consistently.To read in data into MATLAB workspace for further chemometric analysis, the matNMR script matNMRReadBrukerSpectra was exploited as a part of an in-house routine developed in order to automate this process. In this way, the 1H-NMR-based metabolomics studies using a reduced data approach [1H-NMR spectra to extract the information related to the differences in the metabolism between schizophrenia patients and healthy control group. In order to perform chemometrics on NMR spectral data, all peaks in NMR spectra originating from the same chemical surrounding inside the metabolite were aligned. In the first step, shifting the whole spectrum according to the reference signals in the regions 5.12 to 5.19 ppm and 5.20 to 5.35 ppm was performed using 4 iterations, and the target spectrum was chosen from the current data set. For blood samples, an α-D-glucopyranose anomeric doublet, centered at 5.23 ppm, just on the right side of a broad lipid olefinic resonance at 5.27 ppm was chosen [In contrast to approach ,46,47, ts chosen . AlthougThe region between 4.35 and 5.0 ppm was excluded from the data sets before further data pretreatment. Spectra were baselined with a 1st order polynomial baseline function and fitted to predefined regions free of peaks, which were then subtracted from the original spectra. Probabilistic quotient normalization (PQN)  was usedNext, 7-fold contiguous block CV was adopted for all models, while the size of each block was evaluated in relation to an n-fold CV: N/(3 · s), where s is the number of triplicates, and N is the total number of samples in the data set. For the initial number of samples and 7-fold CV, the size of each block was 13 (patients) · 3 (triplicates) = 39 samples. After removing the outliers, the size of the block was recalculated, keeping the triplicates from the same patients in the same block. In order to preserve the triplicate ordering structure of samples, we have slightly modified the default method of sample assignation to the particular blocks arranged for calibration and validation during the CV. The pre-request for such a step was introducing a separate class variable with assigned triplicate grouping. Randomly shuffling of the samples allowed them to reorder inside the data set with respect to the grouping class variable before each CV step. In this way, we preserved triplicate structure during the shuffling of samples and subsequently assigned them to both calibration and validation blocks of samples.The data were examined for natural clusters and outliers by principal component analysis (PCA). Orthogonal partial least squares discriminant analysis (OPLS-DA) was used to classify samples according to clinical and prognostic factors. All spectral variables were mean-centered before PCA analysis. In addition, OPLS-DA was introduced for weight centering. When the group sizes were unequal, the boundary between groups in PLS models were shifted toward the larger group and misclassified many samples. To correct this, the average of the means of the two groups was subtracted from the data matrix, that is, (XA + XB)/2, from the columns .Analyzing the obtained loading plots of mean-centered variables of the data matrix provided straightforward identification of varying metabolite presence in the samples due to their covariance structure and their similarity . However1H-NMR spectra  were obtained using the presaturation pulse program, Watergate (p3919), with 128 scans, 32K data points and a bandwidth of 12 kHz. The methyl of lactate at 1.33 ppm  was used as a referent signal. In addition, we used CPMG (Carr-Purcell-Meiboom-Gill) and T2 edited NMR spectra. Additionally, 2D experiments, such as HSQC and TOCSY, were used to confirm the assignments of molecules. The TOCSY experiments used a mlevphpr.2 spin-lock scheme for 1H-1H transfers. For this experiment, 512 increments with 32 scans were collected. The HSQC experiment was recorded with 256 increments and 120 scans. Together with the 2D experiment assignments and interpretation, the literature and available databases, such as HMDB (Human Metabolome Database), were used to assist in the assignment of molecules.NMR experiments were performed on a Bruker Avance III 500 NMR spectrometer equipped with a 5 mm BBI probe head at 25 °C. Based on serum metabolomics by NMR of a cohort of schizophrenia patients from Serbia, a set of 26 biomarkers for schizophrenia was established. An important discovery is that a great majority of the identified metabolites are equal to the previous reports in Brazil and China on schizophrenia, which opens up a possibility for using these biomarkers as disease markers for diagnostics purposes. Furthermore, four metabolites, aspartate (aspartic acid), lysine, 2-hydroxybutyric acid, and acylglycerols, were identified for the first time in serum samples from this Serbian cohort of patients with schizophrenia based on NMR analyses associated with chemometrics. It is still necessary to discover the universality of the serum biomarkers for schizophrenia independently of geographical and ethnic factors, and for that, a unified analysis of data is necessary.


Post-transcriptional RNA modifications are involved in a range of important cellular processes, including the regulation of gene expression and fine-tuning of the functions of RNA molecules. To decipher the context-specific functions of these post-transcriptional modifications, it is crucial to accurately determine their transcriptomic locations and modification levels under a given cellular condition. With the newly emerged sequencing technology, especially nanopore direct RNA sequencing, different RNA modifications can be detected simultaneously with a single molecular level resolution. Here we provide a systematic review of 15 published RNA modification prediction tools based on direct RNA sequencing data, including their computational models, input–output formats, supported modification types, and reported performances. Finally, we also discussed the potential challenges and future improvements of nanopore sequencing-based methods for RNA modification detection. Most existing methods for RNA modification profiling are based on 2nd generation sequencing technology that requires reverse transcription, in which the RNA modification information is removed from the sequencing signal. Therefore, either antibody enrichment or specific chemical treatment was combined with the standard NGS procedure to selectively identify RNA molecule modification sites cis-face to the positively charged trans-face of the membrane through the nanopore (Nanopore sequencing technology and its supporting nanopore sequencer MinION were developed and provided by Oxford Nanopore Technology (ONT) nanopore  [24], [2The base calling procedure is mainly realized through deep learning methods such as the recurrent neural network (RNN) Beyond the primary sequences, the nucleotide modification sites can be predicted directly from the read-level electrical signal by identifying the “deviant” from the regular signal shifts of the nucleotide . To impl22.1Differential statistical testing and Bayesian modeling are two major types of statistical methods used in RNA modification prediction from nanopore sequencing. The input of differential testing depends on the site-level information, which is often the coverage data of the base called reads or the extracted current signals mapped at the reference sequence. The sites level information is often summarized into contingency tables and subjected to statistical testing a. Bayesi2.1.16A modifications by finding differences in the base calling error rate between the target sample and the reference control sample. The input data is the stacked read coverage at the potential modification site labeled by base call errors. Specifically, the counts of five bases called results  are aggregated from each sample. An n × 5 contingency table is then generated for each base, where n denotes the number of samples and 5 represents the types of base calling results. A G-test is performed on this table to identify any base with a significant change in error rates. An additional G-test is performed on significant bases between other pairings of replicates in the same condition. Benjamini-Hochberg method with an 0.05 FDR threshold is used for multiple testing corrections. If the sum of the G statistics in the lateral G-test is larger than the G statistic in the former one, the base will be excluded from the candidate sites to improve stringency. Subsequently, the log2 fold changes of mismatch to match ratio at the candidate sites between different-conditioned samples are calculated. A base with a log2 fold change >1 is considered a modification site. DiffErr was tested on the nanopore direct RNA sequencing (DRS) data of Arabidopsis. The result showed that the m6A modification sites revealed by DiffErr are enriched in 3′ UTRs but not enriched around stop codons, which is consistent with the pattern of the paired miCLIP sample. About 66 % of the m6A modification sites discovered located within 5 nucleotides of miCLIP sites.DiffErr 6A DRACH motif. The predicted modification sites also overlapped extensively with peaks detected by MeRIP-Seq.The testing method used by DRUMMER is also G-test on a 2 × 5 contingency table from the counts of basecall information for each base. A Bonferroni approach is used to adjust multiple-hypothesis testing. The two rows in the contingency table represent the methyltransferase knockout samples versus the wild-type samples. The fold change of mismatch to match ratio is then calculated at each included base. Candidate modification sites are selected if the site has a onefold or greater change and the adjusted G-test p-value is less than 0.01. This algorithm was tested on the nanopore DRS data of the wild-type (WT) and METTL3-knockout (M3KO) A549 cells infected with adenovirus serotype 5 (Ad5) with matching MeRIP-Seq data. Their results showed that approximately 83.1 % of the universally predicted sites across all datasets were located on AC motifs, with the remaining sites located within four nucleotides away from AC motifs. The observed distances were much smaller than random shuffled sites, suggesting a positive motif association. The sequence logo generated from the context of these candidate sites resembled the commonly known m6A, m1A, 5moU, Psi, m7G, Ino, hm5C, and f5C. The results showed that the best AUROC for Curlcakes data is 0.758 on all possible 5-mers and 0.973 on DRACH motifs. All the IVT data had AUROC >0.74 except for the one containing m5C modification. In addition, ELIGOS was further validated on DRS data of rRNAs from human cells, yeasts, and E. coli. The resulting AUROC were 0.895–0.938, 0.861–0.962, and 0.862–0.953, respectively.ELIGOS 2.1.26A modification sites by comparative analysis between experimental and control datasets. The median signal intensity and the log10 of dwell time of each read are extracted from raw current signals and used as input data. Pairwise comparisons are conducted between modified and control samples on all the possible 5-mers by the robust univariate test (Kolmogorov-Smirnov test) or bivariate Gaussian mixture model (GMM) with a logistic regression test. Nanocompore was first evaluated on in-silico generated data by simulating modified and unmodified current signals. This simulation was based on the current distribution from an IVT human DRS dataset. The unmodified current signals are mimicked by a probability density generator. The simulation of current signals for modified bases is generated by shifting the mean of density distribution. The evaluation results showed that the KS test has the highest sensitivity but with the expense of lower specificity, while the GMM models reach the lowest False Positive Rate and the highest F1 score. GMM models outperformed KS tests when the analysed transcripts had different sequencing depths and were therefore used in the following analysis. In a cell line independent validation test, Nanocompore was applied on the nanopore RNA sequencing data of shRNA-mediated METTL3 knock-down (KD) MOLM13 cell line and wild type (WT) MOLM13 cell line. The results showed that the m6A modification sites predicted were enriched near the stop codons of mRNA, resembling the pattern previously reported by the METTL3-dependent m6A sites. Compared with the miCLIP data of the MOLM3 cell line, 54 % of the modification sites predicted by Nanocompore overlapped with the miCLIP in WT cells and there exists a significant decrease in Nanocompore predicted modification sites at miCLIP crosslink sites. The methylation ratios at these sites are also significantly reduced from the WT to the KD cell line. Although Nanocompore has only been tested and validated only on m6A modification, it is potentially applicable to any modification detection with proper control samples free of modification.Nanocompore 6A modification sites through a multi-sample Bayesian Gaussian mixture model. The model takes input from the normalized mean of the current signals from all reads across different samples at each possible 5-mer. The two components of the mixed distributions are used to denote the unmodified and modified RNA species. The cluster closer to the theoretical distribution for unmodified RNA species is considered unmodified, while the other is modified. A Bayes classifier is used on the posterior probability of the soft clustering to predict modification events at the read level. The modification rate for a given site is estimated by the proportion of modified reads aligned at that position. xPore was tested on the nanopore DRS data of wild-type (WT) and METTL3 knockout (KO) HEK293T cell lines with matched m6ACE-Seq data as reference labels. Differential analysis was then applied to the estimated modification rates of replicates between WT and KO to determine whether the site is modified. Benjamini-Hochberg method with a 0.05 FDR threshold is utilized to adjust the multiple testing P-values. At the A-centered positions (NNANN), xPore achieved an AUROC of 0.86 and a precision of 0.6. The low precision could be a result of detecting modifications that were missed by antibody-based prediction. Over 90 % of the modification positions predicted with a p-value lower than 0.001 overlapped the modified DRACH motifs identified by m6ACE-Seq. The predicted sites formed a distribution peak near the stop codon, resembling the pattern identified by m6ACE-Seq. xPore also demonstrated a positive correlation between the quantified m6A levels in the HEK293T cell lines and the profiles acquired from MAZTER-Seq and m6ACE-Seq, with Spearman's rank correlation coefficient equal to 0.49 and 0.66, respectively. xPore has an advantage over other methods in that it can obtain the quantitative m6A levels from individual DRS experiments without depending on the associated KO samples.xPore predicts m6A modification sites identified were located at m6A consensus motifs and almost all the modification sites predicted were in 3′UTRs. Compared with miCLIP data, 84.1 % of the predicted modification sites were located within 5nt of the nearest miCLIP site. The authors also intend to improve Yanocomp to enable the prediction without the need for paired KO experiments.Yanocomp constructs GMM based on the mean current values of each kmer at each read. The model contains two Gaussian components and one uniform component. The weights for the two Gaussian components are also utilized to estimate the modification rate at each position, followed by a G-test as the differential analysis between control and experiment samples. This algorithm was tested on the DRS data of Arabidopsis defective in the function of VIRILIZER (vir-1) and VIR complemented lines (VIRc). The average modification rates dropped by 81.8 % in the defective cell line compared to the WT condition. Most of the m2.2The supervised learning-based prediction methods often use heterogeneous input information, in which both the current intensity and the base called sequence are input features. This can often provide a performance advantage over statistical methods which typically use only one feature type. The adopted models or algorithms include K-means 6A-modified bases based on systematic base-calling errors in DRS datasets. The features are composed of per-site and per-kmer quality, mismatch, insertion, deletion, and current intensity. It can either uses absolute feature values or differences in feature values between two comparing samples. The SVM model was trained and tested on Curlcakes datasets with features of per-base quality, mismatch, and deletion at the middle position of the RRACH k-mers. The Curlcakes datasets, originally designed and generated by the authors of EpiNano, consist of the DRS data of the modified and control IVT sequences containing all possible 5-mers. Two biological replicates were generated for both modified and control datasets. The SVM model was trained on the 1st replicate of Curlcake datasets and tested on the 2nd replicate. The results showed that the AUROC were higher than 0.969. For cell line independent validation, the model was trained and tested on the nanopore DRS data of polyA(+)-selected RNA from wild-type (WT) and ime4 knockout (KO) yeast strains with distinct subsets of features. The model reached the AUROC higher than 0.613 with absolute feature values and 0.636 with differences in feature values. In the recent update EpiNano 1.2, the authors implemented another mode named EpiNano-Error, which predicts modification sites by testing for differences in feature values between two comparing samples. Z-score deviances and data point residuals after fitting the linear regression models are used to determine the modification sites. The Bonferroni method is used to correct for multiple testing p-values. The threshold parameters can be user-defined.Among these machine learning-based methods, EpiNano-SVM 6A sites on DRACH motifs through a random forest model using the features of modification stoichiometries within a 20-bp region. The centre position (Position 0) of the region is an adenine nucleotide in the middle of the DRACH motifs. The modification stoichiometries are calculated by Tombo by averaging the modification probabilities of each read. The random forest models were trained and tested on the HEK293T cell line using labels from matched m6ACLIP-seq datasets. The DRACH motifs containing overlaps with m6A modification sites in label datasets were considered as positive sites and otherwise negative sites. The obtained results reached the accuracies ranging from 67 % to 83 %, the precisions ranging from 40 % to 92 % and the AUROC ranging from 54 % to 76 % for all 18 models over instances of DRACH motif. Among them, only 4 models  had AUROC beyond 0.67. In the independent validation on the HMEC cell line, the m6A sites predicted by MINES were enriched near the start of 3′UTR across all transcript isoforms, which resembled the results from typical m6A-seq. In the future, MINES may upgrade to realize read-level prediction.MINES classifies the m6A sites and it achieved an AUROC of 0.68 on all possible 5-mers. Most modification sites within DRACH motifs were successfully detected. Currently, nanoDoc cannot perform read-level predictions for modification rate quantification. In the future, nanoDoc will accumulate more data regarding different modification types in the 5-mers and will be independent of the IVT reference data.nanoDoc distinguishes RNA modifications at the site level through a combination of deep learning and machine learning. It adopts a Deep One-Class (DOC) classification model with transfer learning and takes normalized current signals and the dwell time of each possible 5-mer as the input feature. Two parallel DOC classification models are constructed to calculate the Euclidean distance between the output vectors of target 5-mers in the secondary network and output vector of other 48 similar 5-mers in the reference network. The obtained Euclidean distances constitute a distribution for each 5-mers. The reference network was trained on IVT data free of modification. Besides, the IVT data was also used as the control dataset to calculate the distance distributions. During the application of nanoDoc, two pairs of parallel DOC classification models were processed simultaneously. The distribution of distances between two IVT read datasets (control distribution) and the distribution between an IVT read dataset and a native sequence read dataset (native distribution) were generated, respectively. Next, these two distributions were scored through cumulative distribution functions. With the modification present, the score of the control distribution was expected to be greater than that for the native distribution. To distinguish between different classifications, the native sequence 5-mer data were processed through a reference classification network followed by K-means clustering. nanoDoc was tested on the nanopore DRS data of native rRNA of E. coli and yeast with matched reference IVT sequencing data. It tried to detect all 23 kinds of RNA modification and achieved an AUROC of 0.96. Within 7 5-mers, 10 out of 15 types of modifications were successfully identified with>50 % specificity. nanoDoc was also applied on Curlcakes data to predict mNanoRMS 6A modification sites at RRACH motifs. The input features of the XGBoost model include the median, standard deviation, mean, and dwell time of the current signals over each RRACH motif extracted by the Tombo re-squiggle function. Nanom6A was trained and tested on the Curlcakes dataset. The authors obtained an AUROC of 0.97 under 10-fold cross-validation, and approximately 91–96 % of modification sites are uncovered. For cell line independent validation, the prediction over the wild type (WT) and METTLE3 knockdown (KD) HEK293T cell line were compared with the matched SCARLET and MeRIP-Seq data. In addition, the Arabidopsis mutant defective in the function of VIRILIZER (vir-1) and VIR complemented lines (VIRc) and stem-differentiating xylem (SDX) of Populus trichocarpa with matched MeRIP-Seq and m6A-REF-seq data were also compared. In the HEK293 cell line, the numbers of predicted m6A modification sites and the predicted modification rates showed a decline in KD samples at ACTB sites, resembling the results reported from SCARLET and MeRIP-Seq. In the Arabidopsis cell line, 40 % of the m6A modification sites predicted were shared with those detected by EpiNano or MINES, and 66 % of the RRACH motifs with predicted modification sites were also predicted by DiffErr. The number and modification ratios of m6A sites detected by Nanom6A dropped in the VIRc relative to the vir-1. In the Populus trichocarpa, Nanom6A predicted m6A with a similar enrichment pattern compared with the MeRIP-Seq and m6A-REF-seq. Approximately 81 % and 80 % of the modification sites predicted overlapped with MeRIP-Seq and m6A-REF-seq, respectively.Nanom6A uses the XGBoost model to predict mPenguin predicts pseudouridine modification at all possible 5-mers by assembling three models of Support Vector Machine (SVM), Random Forest (RF), and Neural Network (NN). It uses Nanopolish to extract current signal features as the model input, including the mean, standard deviation, dwell time of the current signals, and the k-mers of the events aligned to the reference genome. Penguin was trained and tested on the nanopore DRS data from the HEK293 cell line, with the matched benchmark dataset as labels. The obtained results showed that the SVM  had a higher accuracy, recall and AUROC and a lower precision compared to RF . The SVM also achieved comparative results with NN . In the cell line independent testing on the nanopore DRS data of HeLa cell line, NN  outperformed SVM  in all aspects. RF was excluded from the results due to its low accuracy (<0.5). Penguin uncovered pseudouridine modification sites from 7148 genes at 6482 unique genomic locations shared across the HEK293T and HeLa cell lines. Among the top 1 % frequent pseudouridine modification genomic locations in the HEK293T and HeLa cell lines, 15.8 % were commonly detected across both cell lines. In the future, Penguin may integrate advanced deep learning methods for modification prediction.6A modification on RRACH motifs through Bidirectional Long Short-Term Memory (Bi-LSTM) neural network. A total of 12 Bi-LSTM neural networks are constructed for each RRACH motif. It extracts the mean, median, standard deviation, dwell time, and base quality of the current signals at RRACH motifs as input features through the Tombo re-squiggle algorithm. DENA is the first neural-network-based RNA prediction tool trained on in vivo transcribed mRNA data. The network was trained and tested on the nanopore DRS data of Arabidopsis defective in the function of VIRILIZER (vir-1) and VIRILIZER complemented cell lines (VIRc). The AUROC and the accuracies of all the 12 models reached between 0.90 and 0.97 and between 0.83 and 0.93, respectively. For validation, DENA was applied on the wild-type (Col-0) Arabidopsis and full training dataset (vir-1 and VIRc). A large fraction of overlap was observed between the predicted modification sites in Col-0, VIRc, and vir-1 cell lines. A high correlation between modification rates was discovered between VIRc and Col-0. The m6A sites predicted were also enriched near the stop codon and 3′UTR, similar to the expected distribution of m6A modifications.There are three methods using deep learning models for modification sites and quantification prediction, which are DENA m6Anet is a prediction software based on Multiple Instance Learning. The model consists of two joined modules of a read-level encoder and a pooling layer. The encoder converts the input features into a high-dimensional representation, followed by the read-level prediction of modification rates with two hidden layers. The noisy-OR pooling layer integrates the read level prediction to estimate the probability of modification at the site level. The normalized mean, standard deviation, and dwell time of the nanopore raw signals of each read at each position (i) and the single position before (i − 1) and after (i + 1) are used as input features in this model. The sequence information of all possible 5-mers in the training data is encoded as a two-dimensional vector. The embedding is also used as an input feature for the model. The labels for the model are the binary modification status at each site marked with 1 (modified) or 0 (otherwise). The modification status can be observed from other sequencing methods such as m6ACE-Seq or other gold-standard data. The training data for m6Anet was the nanopore DRS data of the HCT116 cell line and the training labels were the matched m6ACE-Seq data. Sites mapped to non-DRACH motifs were all removed from training data. The trained m6Anet model was evaluated on the HEK293T cell line using the ground truth of the matched m6ACE-Seq and miCLIP data. The results showed an AUROC of 0.83 and a PRAUC of 0.35 on DRACH motifs, outperforming EpiNano and Tombo. m6Anet also achieved the AUROC of 0.83 and 0.83 and PRAUCs of 0.43 and 0.37 on 4 motifs  and all RRACH motifs, respectively, outperforming MINES and nanom6A. m6Anet can generalize well to new DRS data without the requirement of new training data.nano-ID predicts the sites of nucleoside analogues 5-Ethynyluridine (5EU) at the read level via a neural network model. 5EU is a chemical modification used to identify newly synthesized RNA, which is often generated through metabolic RNA labelling. The neural network takes the normalized raw current signals, base-calling event probability, and alignment mismatch properties as the input features. The network architecture is constituted of a batch normalization layer and two dropout layers between the three dense layers. nano-ID was trained on the nanopore DRS data of the myelogenous leukemia K562 cell line. The cells were exposed to 5EU labelling for 24 h (5EU 24 h), exposed to 5EU labelling for 60 min (5EU 60 min), or not exposed to 5EU labelling (Control). The trained network achieved an accuracy of 0.87 and an FDR of 0.025 on the 5EU 60 min cell line. The model achieved the AUROC of 0.94 overall and 0.96 for reads with lengths >500nt and 1000nt. Similar results were obtained from a random forest classification with the same training and testing data. Improvements of both the nanopore sequencing platform and the base calling algorithm are expected to further improve the accuracy of Nano-ID.3Epitranscriptomics is a field that has been profoundly advanced in recent decades. The research momentum is mainly driven by the breakthrough of sequencing technologies that enabled the omic-level analysis of RNA modifications. With the recent development of third-generation sequencing technology, nanopore direct RNA sequencing has the potential to realize the base resolution detection of RNA modification over long native RNA sequence reads independent of sequencing biases introduced by PCR amplification and reserve transcription.D/KO experiment. In contrast, machine learning and deep learning models can generalize the signal pattern learned from the training data set without requiring the paired no-modification control sample.Here we have comprehensively reviewed 15 published RNA modification prediction tools based on direct RNA sequencing data. Among them, 6 use statistical models and 9 are based on supervised machine learning . The pri1ψ Regarding the supported modification types of the prediction methods, most of the methods (12/15) are trained and applied to only one type of modification . HoweverSequencing depth is also a strong factor influencing the detection power of modification sites, especially for the prediction tools based on statistical models. If the sequencing depth is too low, the differential testing can be significantly affected by the reduction of statistical power, especially considering the relatively low sequencing accuracy of Nanopore sequencing. Common ways of reducing the high statistical noise of low sequencing depths are pooling reads across replicates 6A. Mistakes generated from low-quality reads and poor alignment results are also potential causes of false positivity. In the framework of Bayesian models, one way to reduce false positives is to only consider signal shifts in one direction. Eventually, for all the computational frameworks, a trade-off is always expected between accuracy and precision upon different cutoffs of predicted values. Therefore, many variations between the prediction outcomes of different methods can be attributed to the difference in the stringencies of the default thresholds.Furthermore, all the currently reported methods are not free of false positive methylation calls due to various reasons. Most of such false positivity may be attributed to the overfitting caused by the confounded training samples. Besides, the low differentiability of the electrical signal under certain k-mer contexts may inevitably introduce the sequence-dependent prediction error. Some signal shifts resulting from modifications are observed near the true modification sites, which are also likely to introduce false positive calling. One possible approach to lowering false positivity is to narrow the candidate sites only on consensus motifs such as DRACH for mhttps://github.com/XichenZhao0223/Nanopore-tools-comparison. There is also a call for defining robust evaluation metrics and strong benchmark datasets, as most currently published tools have used different pre-processing and evaluation schemes  the preparation of high-quality, unconfounded IVT training data. 2.) the upgrade of the 3rd generation sequencing platform that provides more differentiability between signals of modified and normal bases. Meanwhile, the new computational models can be improved both in accuracy and computational efficiency . If the above criteria have been met, the refined computational tools can eventually enable the detection of all modification types simultaneously within a single RNA molecule. Such detection power can shed light on important epitranscriptomic phenomena, such as revealing the interaction between different modification types on a single molecular transcript. From a long-term perspective, RNA modification detection based on direct RNA sequencing is a promising novel technique to improve our understanding of epitranscriptomic.The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.


Cisplatin, a platinum-based chemotherapeutic agent, is widely used as a front-line treatment for several malignancies. However, treatment outcomes vary widely due to intrinsic and acquired resistance. In this study, cisplatin-perturbed gene expression and pathway enrichment were used to define a gene signature, which was further utilized to develop a cisplatin sensitivity prediction model using the TabNet algorithm. The TabNet model performed better (>80 % accuracy) than all other machine learning models when compared to a wide range of machine learning algorithms. Moreover, by using feature importance and comparing predicted ovarian cancer patient samples, BCL2L1 was identified as an important gene contributing to cisplatin resistance. Furthermore, the pharmacological inhibition of BCL2L1 was found to synergistically increase cisplatin efficacy. Collectively, this study developed a tool to predict cisplatin sensitivity using cisplatin-perturbed gene expression and pathway enrichment knowledge and identified BCL2L1 as an important gene in this setting.  Although all platinum-centered analogs function by binding DNA, the effectiveness of individual analogs is cancer subtype-specific. Cisplatin, which is the base drug and widely used in the treatment of several cancers, displays differential responses due to intrinsic and acquired resistance.Intracellular cisplatin accumulation correlates with acquired resistance, which is dependent on the influx and efflux of the drug -) in cisplatin are replaced by water molecules. The resulting electrophilic molecule is considered active cisplatin and can react with nucleophilic groups on DNA and sulfhydryl groups on proteins In an aqueous environment, chloride ions  that can predict cisplatin sensitivity et al. identified four genes  et al., applying a regularized logistic regression model to multi-omics data, identified six genes  associated with in vitro cisplatin sensitivity Considering the complexity of cisplatin’s interactions with the cellular environment and its effectiveness, several studies have recently attempted to build cisplatin response prediction models based on specific gene signatures. Using a 15-gene signature , a support vector machine (SVM), and data from The Cancer Genome Atlas (TCGA) patients with bladder, ovarian and colorectal cancer, Mucaki 22.150 <5 µM) cell lines were used to determine pathway enrichment based on parametric gene set enrichment analysis (PGSEA) scores Cisplatin is known to activate several signaling pathways regulating various cellular events 2.2Cisplatin-induced changes in gene expression were subsequently determined to develop cisplatin sensitivity prediction models. Gene expression changes upon cisplatin treatment are highly heterogeneous within cell lines and in ovarian cancer patients. Thus, gene expression changes upon cisplatin treatment in highly sensitive cell lines  were combined with gene expression changes in patients displaying upregulation/downregulation of WNT/β-catenin and SHP2-signaling. The merging of both sets of gene expression change data collected highly variable genes, which resulted in the identification of a 720-gene signature A. With t2.3The TabNet model was next compared to a panel of machine learning algorithms. As shown in 2.4.Next, important features that contribute to the prediction of cisplatin sensitivity were discerned. The built-in feature importance function was used for the TabNet model, while SHAP values 2.5It was next hypothesized that inhibitors of pro-survival proteins identified by feature importance may play a role in ovarian cancer cell growth. A-1155463 (BCL2L1 specific), navitoclax , ON1231320 (PLK2 specific), and TCF4/β-catenin complex antagonist (LF3 and toxoflavin) were the inhibitors used. Four ovarian cancer cell lines displaying differential responses to cisplatin (ES2 >A2780 >SKOV3 >HEY) were treated with specific inhibitors for 48 h. All cell lines exhibited differential sensitivity to different inhibitors A. Toxofl3In this study, a panel of genes that can predict cisplatin sensitivity based on gene expression data was identified using pathway enrichment and drug-perturbed gene signatures. Initially, a wide range of machine learning algorithms was employed. While several of these, such as ridge, lasso, elastic net, random forest, and XGBoost displayed considerable predictive performance, TabNet demonstrated superior performance. Further analysis was also performed to identify important features and observed synergistic effects when cisplatin was used with BCL2L1, PLK2, and TCF4 inhibitors.Cells treated with cisplatin displayed enrichment in the WNT/β-catenin pathway and SHP2 signaling A-B, a phIn previous research, only six to 15 genes were used to predict cisplatin sensitivity Both the quality of pharmacogenomic data and the number of samples are crucial for constructing a predictive model using machine learning algorithm In line with the pathway enrichment data, using feature importance from multiple prediction models, BCL2L1 has been identified as a key gene mediating cisplatin resistance A-C. Earl44.1The ovarian cancer cell line HEY was from Tebu-bio, while SKOV3 (SK-OV-3) and ES2 (ES-2) cell lines were from Sigma-Aldrich and ATCC, respectively. The A2780 cell line was kindly given by Dr. Chris D. Madsen (Lund University). Human ovarian adenocarcinoma cell line, A2780 was cultured in RPMI-1640 supplemented with 10 % FBS and 2 mM glutamine 4.2Cells were seeded in six-well plates a day before the addition of cisplatin. Cisplatin was dissolved in water and added 24 h before lysis. Cell lysates were mixed with 2X SDS sample loading buffer, and 10 μg total proteins from each lysate were separated on SDS-PAGE gels. The separated proteins were then transferred to polyvinylidene difluoride (PVDF) membranes and probed with specific antibodies. Anti-phospho-ERK1/2 and anti-phospho-AKT antibodies were from Cell Signaling Technology. The anti-GSK3β-Ser9 antibody was from Thermo Fisher Scientific, while the anti-ACTB antibody was from Santa Cruz Biotechnology.4.350 < 5 µM or IC50 < 5 µM of cisplatin was considered as sensitive cell line. TCGA annotations were used for ovarian cancer patient data.Gene expression data were obtained from gene expression omnibus (GEO) 4.4To develop binary classification models, gene expression data (cell lines and ovarian cancer patients) from different platforms were combined. Both microarray and RNAseq data were used, and all data were log2-normalized. In all experiments, 80 % of the data were used for training , while 20 % were used for testing. Jupyter Notebook was employed to execute Python commands. For TabNet, the PyTorch framework was applied, and the model was developed on CUDA. All other machine learning programs were run on the CPU. The majority of machine learning models used are available at sklearn (scikit-learn.org). Logistic regression, passive-aggressive classifier, linear perceptron classifier and stochastic gradient descent (SGD) classifier were used from sklearn.linear_model with different regularization parameters. Bernoulli Naive Bayes, complement Naive Bayes classifier, Gaussian Naive Bayes classifier, multinomial Naive Bayes classifier were from sklearn.naive_bayes. The decision tree classifier and Extra tree classifier were from sklearn.tree. From sklearn.ensemble several tree-based models including ADA boost classifier, bagging classifier, extra trees classifier, gradient boosting classifier, histogram-based gradient boosting classifier, and random forest classifier were used. The nearest centroid classifier, k-nearest neighbors classifier, and radius neighbors classifier were from sklearn.neighbors. Support vector machine algorithms linear support vector classifier, support vector classifier, and nu-support vector classifier were from sklearn.svm. Other models include the Gaussian process classifier (sklearn.gaussian_process), multi-layer perceptron (MLP) classifier , calibrated classifier CV  with Gaussian Naive Bayes (sklearn.naive_bayes) estimator, and sklearn-wrapped version of Cat boost classifier 4.5To measure drug sensitivity and drug synergy, A2780, ES2, HEY, and SKOV3 cells were seeded in a 96-well plate. Drugs were diluted using serial dilutions with a 10 µM highest concentration for A-1155463, ON1231320, and navitoclax, and 50 µM highest concentration for cisplatin, LF3, and toxoflavin. Eight different concentrations were used to treat cells for 48 h. Cell Titer-Glo (Promega) was used to measure cell viability following drug treatment. For synergy, 1:1 drug combinations were utilized, and then full combination (8 ×8) values were predicted by DECREASE 4.6All raw data are available upon request to the corresponding author. Several tools and databases used for data collection and processing are publicly available. Random forest feature selection: sklearn.feature_selection.SelectFromModel (scikit-learn.org); ORCESTRA: www.orcestra.ca.www.pharmacodb.ca; GEO: www.ncbi.nlm.nih.gov/geo; GDSC: www.cancerrxgene.org; Network Analyst 3.0: www.networkanalyst.ca.PGSEA: rdrr.io/bioc/PGSEA; DECREASE: decrease.fimm.fi; DepMap: www.depmap.org; Synergy Finder: synergyfinder.fimm.fi; cBioPortal: www.cbioportal.org; PharmacoDB: Ahmad Nasimian: Performed experiments, analyzed data, and wrote the manuscript, Mehreen Ahmed: Performed experiments, analyzed data, and wrote the manuscript, Ingrid Hedenfalk: Analyzed data and wrote the manuscript, Julhash U. Kazi: Performed experiments, analyzed data, supervised the research, designed the study, and wrote the manuscript.The authors declare no conflicts of interest.