Accurate prediction of breast cancer metastasis risks using gene expression data and machine learning can help improve cancer treatment and overall survival. However, breast cancer can be categorized into multiple subtypes, and a single predictive model may not work well for all patients. In this work, we propose a computational method to construct personalized models, where the key is to select a group of patients to train a different model for each testing patient. Experimental results on multiple datasets showed that the proposed method, termed Personalized Classifier with Multiple Thresholds (PCMT), achieved significantly better prediction accuracy than existing algorithms that train classifiers using all available patients or using patients belonging to a predefined subtype. In addition, the top features identified by PCMT are robust across different datasets, and include genes that are well known to be associated with subtype-specific metastasis.Accurate prediction of breast cancer metastasis in the early stages of cancer diagnosis is crucial to reduce cancer-related deaths. With the availability of gene expression datasets, many machine-learning models have been proposed to predict breast cancer metastasis using thousands of genes simultaneously. However, the prediction accuracy of the models using gene expression often suffers from the diverse molecular characteristics across different datasets. Additionally, breast cancer is known to have many subtypes, which hinders the performance of the models aimed at all subtypes. To overcome the heterogeneous nature of breast cancer, we propose a method to obtain personalized classifiers that are trained on subsets of patients selected using the similarities between training and testing patients. Results on multiple independent datasets showed that our proposed approach significantly improved prediction accuracy compared to the models trained on the complete training dataset and models trained on specific cancer subtypes. Our results also showed that personalized classifiers trained on positively and negatively correlated patients outperformed classifiers trained only on positively correlated patients, highlighting the importance of selecting proper patient subsets for constructing personalized classifiers. Additionally, our proposed approach obtained more robust features than the other models and identified different features for different patients, making it a promising tool for designing personalized medicine for cancer patients. Breast cancer is a leading cause of cancer-related death in women, while the majority of breast cancer patients have a good prognosis after therapy, about 30% of early-stage breast cancer patients will experience distant metastases, and 90% of patient deaths are because of complications from metastases . High-thIt is well established that cancer is a heterogeneous disease, which hinders the overall performance of metastasis prediction models. Breast cancer can be commonly divided into five subtypes , each categorized by different molecular characteristics and prognosis ,21,22,23In this paper, we propose a novel method, termed personalized classifier with multiple thresholds (PCMT), to address the issues mentioned above. To predict the metastasis risk for a target patient, the algorithm first selects a group of training patients whose similarities to the target patient are the highest or lowest, and then train a classifier using only these selected patients to make a prediction for the target patient. Our results showed that PCMT achieved significantly better prediction accuracy compared to logistic regression and random forest models trained on the whole dataset in several cross-validation schemes using multiple independent datasets. Additionally, PCMT outperformed subtype-specific models trained only on patients of the same subtype. Our analysis also revealed that including the most similar and the least similar patients in training the personalized classifier improved the prediction accuracy of PCMT compared to classifiers trained only on the most similar patients. Finally, we showed that personalized classifiers improved robustness in identifying top features compared to the base models and that the personalized classifier was able to generate different top genes for different subtypes, which was not possible by using the base models.For model training and testing, the gene expression dataset is collected from the Amsterdam Classification Evaluation Suite (ACES) . This daTo further evaluate the feasibility of our proposed algorithm, we used another dataset independent from the ACES dataset. The NKI gene expression dataset was obtained using the Affymetrix HG-U133a platform and contains 11,658 genes and 295 patients [To assess whether the classifiers built using microarray data can be used to make predictions on newer data generated with the RNAseq technology, we downloaded TCGA prognostic dataset . We consFor this study, we only considered patients with either “good” or “poor” outcomes. After applying these criteria, the final dataset had 225 patients with good outcomes and 123 with poor outcomes.The TCGA gene expression dataset consisted of a total of 13,494 protein-coding genes, of which 9874 are common in the ACES dataset. The TCGA log2(norm + 1) data was first mean centered, and quantile normalization was applied to the combined TCGA and ACES dataset containing only the common genes.Cancer is a heterogeneous disease, meaning many different subtypes exist within the dataset. However, the patients with the same subtype are similar in molecular characteristics. Therefore, we define Personalized Classifier (PC) for a sample as a classifier built on a subset of the training samples selected according to their similarity with that specific sample. Patients with the highest similarity tend to have the same label, which may reduce the generalizability of the learned model. In addition, these patients may have distinct distributions in some feature values, and the resulting model may be sensitive to outliers in the test patient. We included the most similar and most dissimilar patients from the training data to solve this problem. Similarity is measured using the Pearson Correlation Coefficient (PCC) between two feature vectors, where each feature vector contains the expression levels of 12,750 genes for one patient. Other similarity measures may be used, depending on the dataset’s characteristics. To build a PC for a patient, we first calculate the PCCs for the entire training dataset. Then we filtered the similar and dissimilar patients based on a PCC threshold, estimated from the average number of samples in each training data subset resulting from the threshold. Next, the positively and negatively correlated patients whose absolute PCCs are greater than the PCC threshold are selected. Then a Logistic Regression (LR) model has trained on the filtered training patients using the expression levels of the 12,750 genes as independent variables and metastasis status as dependent variables. Finally, the testing patient is classified by the trained LR model. Details are given in Algorithm 1.To build Personalized Classifier with Multiple Thresholds (PCMT), a set of PCC thresholds are used to cover a wide range of training data sizes, reflecting the uncertainty in subtype population size for each testing patient. The PCC thresholds in this paper have been chosen such that for different thresholds, sufficiently different training datasets can be selected. The complete ACES dataset has ∼1600 patients, which can be further grouped into five subtypes with sizes ranging from ∼100 to ∼600. Therefore, we chose six PCC thresholds from 0.15 to 0.275 with a 0.025 increase, which resulted in median training dataset sizes from 71 (PCC = 0.275) to 504 (PCC = 0.15). A smaller (less stringent) threshold would increase the training dataset size and may result in too many training patients and defeat the purpose of personalized classifiers. In contrast, a larger (more stringent) threshold would reduce the training dataset size and cause overfitting.Algorithm 1 Personalized Classifier.Require: PCCs are calculated for each patient in  Identify the patients in  Trained a logistic regression (LR) model using the identified patientsReturn the trained LR model After the classifiers from different PCC thresholds are generated, the testing patient is classified by each of the trained PC classifiers, which generate predicted probabilities. The predicted probabilities from the multiple PC classifiers are averaged to obtain the predicted probability of PCMT for the testing patient. The algorithm details are given in Algorithm 2.Algorithm 2 Personalized Classifier with Multiple Thresholds (PCMT).Require: Get Personalized Classifiers for each of the  Obtain predicted probabilities from each of the PC classifiers for  Average the predicted probabilities of the PC classifiersReturn predicted probability for the  t-test was performed in 5-FCV and LOSO-CV, comparing PCMT with the individual PC classifiers and the base LR and base RF models.To compare the prediction accuracy improvement of PCMT with the base classifiers, two models  and Random Forest (RF)) implemented in the python scikit-learn package were considered. Default parameters were used in all experiments, except that for LR, the L1 penalty was set to true to achieve sparse solutions due to a large number of gene features present in the dataset. Previous studies in the same dataset and similar datasets have shown that RF has consistently outperformed other classifiers in predicting breast cancer metastasis, and its performance is robust concerning parameter variations, making it a preferred choice for benchmarking ,27. The To further evaluate the performance of PCMT and other classifiers, we used the ACES dataset as the training data, and the TCGA or NKI dataset as the testing data, while the experiment is similar to the LOSO-CV evaluation, here only a minimum cross-dataset normalization was performed between the training and testing data. In addition, the TCGA dataset was obtained using the RNAseq technology, while the ACES dataset was obtained using the microarray technology. Therefore, this experiment is expected to be more challenging than the LOSO-CV evaluation on ACES dataset, and may represent a more realistic clinical setting. We note that cross-dataset compatibility is itself a difficult challenge and therefore additional effort will be needed to further improve the cross-dataset normalization procedures in future studies.Another experiment we did was to measure the prediction accuracy for each pre-defined subtype of breast cancer. Patient in the ACES dataset are classified into five subtypes, i.e., basal, HER2, luminal-A, luminal-B, and normal-like, based on the expression patterns of pre-defined gene markers . To evalt-test was performed in 5-FCV and LOSO-CV to compare the PC classifiers trained on positively and negatively correlated patients with the PC classifiers trained on only the positively correlated patients for different thresholds of PC classifiers and PCMT.We performed an additional experiment to show the impact of including the negatively correlated patients in the PC classifier. As the baseline experiment, the results obtained from x features were taken from both the sub-samples for robustness analysis. Finally, the robustness measure was defined as the ratio between the observed number of common top features from the two sub-samples and the expected number of common features by chance [The whole ACES dataset was randomly partitioned into two disjoint sub-samples. Then top y chance . Since tThe number of selected top features is determined by :(1)X=totSplitting ACES into two sub-samples was repeated 20 times so that a vector of 20 overlap ratios was obtained for each model for a specific value of the expected number of overlaps. The number of expected overlaps was kept as 5, 10, 20, 30, and 50, so the top features chosen were 252, 357, 504, 618, and 798 from each model.A single personalized classifier (PC) with a PCC threshold of 0.175 was used for the robustness evaluation. For a single partition of sub-samples, leave-one-out cross-validation (LOOCV) was performed for this specific PCC threshold. Then the learned coefficients were generated for each sample belonging to that partition. The average of the absolute coefficients was calculated to obtain a feature importance score from PC classifiers. The exact process was repeated for the other disjoint partition of sub-samples. This way, features with the highest average coefficient were selected from two disjoint sub-samples. The robustness measure was calculated by taking the ratio between the number of common top features and the number of expected overlaps. As a random control, another version of the personalized classifier was created using random neighbors while keeping the same number of patients as the actual personalized classifier with a PCC threshold set to 0.175. For base LR and base RF, two different models were trained on two disjoint sub-samples, and top features were selected from the model’s feature importance score. Random Forest inherently provides feature importance scores for the features. For LR, the absolute values of the learned coefficients were used for selecting the top features.Model coefficients were generated from personalized classifiers using LOOCV, in which the threshold was set to 0.2. The coefficients of the patients belonging to a subtype are taken into consideration. Then feature ranking was generated by averaging the absolute coefficient. This way, 20 top genes were selected for five subtypes from a personalized classifier. Similarly, coefficients were generated by base LR using LOOCV. Finally, feature ranking was generated by taking the average of absolute coefficient for base LR. Top 20 genes were selected from base LR.t-test). In leave-one-study-out cross-validation (LOSO-CV), PCMT outperformed the base LR and RF models, and the differences are statistically significant. PCMT also achieved better performance than each PCs with limited statistical significance  for LR, RF, and PCMT. The total running time to complete the LOOCV is 17,697 s for LR, 8546 s for RF, and 6353 s for PCMT. As there are 1616 patients, this translates to about 11 s for LR and 5.3 s for RF to build a single model. The time taken to make predictions with LR and RF models is negligible. For PCMT, there are no pre-trained models. The average running time is 4 s per testing instance, which is spent on identifying training samples and constructing multiple LR classifiers with smaller training datasets than in LR or RF. In practice, as testing samples usually do not come in large batches, the increased running time in testing for PCMT should not be a major concern.We hypothesize that including negatively correlated patients in the training data increases diversity within the training data, which enables the personalized classifier to classify the testing patient better. As shown in In this analysis, we examined how personalized classifiers compare to subtype-specific models built using patients belonging to a specific subtype. The prediction accuracy of PCMT, subtype-specific LR and subtype-specific RF classifiers are given in The relatively mediocre prediction accuracy of PCMT in HER2 is likely because the personalized classifiers included much more negatively correlated samples than positively correlated samples (data not shown), hampered the prediction accuracy of PCMT in the HER2 subtype. It is also observable that the patients in the HER2 subtype have lower correlations within themselves and have much higher negative correlations with the patients of different subtypes . In geneTo evaluate whether the features selected by the classifiers are robust, we partitioned the dataset into two disjoint groups, applied different classification algorithms to each partition to select a given number of top features, and counted the number of common top features shared between the two partitions. Model robustness is then defined as the ratio between the observed number of common features and the expected number of common features (see Methods).p-value The robustness of the PC can be attributed to the fact that each component classifier of the PC is learned from a group of generally very similar patients, and therefore, the feature scores are concentrated on relatively fewer features. As a result, the feature score distribution is skewed, with the important features scored much higher than the rest of the features, and feature ranking is robust between the two disjoint patient partitions. In contrast, when the classifier is learned from all patients (as in base LR), due to the high heterogeneity of the training samples, the decision boundary tends to be complex, and many more features are needed to construct a good model. Therefore, the feature scores tend to spread out to many features, which causes the feature ranking to be more sensitive to a small perturbation in the training data. To further explore this possibility, we computed the percent of total unsigned feature scores accounted for by the top 5% features.  For an LR model learned from all patients, the top 5% of features accounted for 16.9% of total feature weights. In comparison, for PC, the top 5% of features accounted for 19.6% of feature weights on average , significantly higher than in the base LR . ImportaA key advantage of our algorithm is its ability to identify patient-specific features that are important for predicting individual metastasis events. For a more concise analysis, here we report the most significant features for each subtype by averaging the feature coefficient vectors belonging to patients in the same subtype. From Since most types of cancer are heterogeneous in nature, and have multiple subtypes that have different molecular and/or clinical characteristics , we beliImproving the prediction of metastasis is crucial to reduce the death risk of breast cancer patients. In this paper, we proposed training personalized classifiers using only a subset of training samples whose gene expression levels are highly correlated with the test patient. Results show that the personalized classifier obtained better prediction accuracy than the base models trained on the complete training dataset as well as models trained with patient in the same intrinsic subtype. Our results also revealed that including negatively correlated patients in the personalized classifier improves the diversity within the model, leading to more accurate predictions when compared to classifiers trained on only positively correlated patients. The top features identified by the personalized models were shown to be more robust than those identified by the base models. Moreover, the personalized models identified different top features for different subtypes, some of which were already shown to be associated with subtype-specific metastasis. It is worth noting that these results are based on one of the largest datasets for breast cancer metastasis prediction consisting of 12 patient cohorts, as well as two additional datasets that have been processed separately. In particular, the cross-dataset evaluation based on TCGA breast cancer data is challenging, as the models were trained on microarray data and tested on RNAseq data. Therefore, the results support that our method has achieved genuine, robust performance gain compared to traditional methods in predicting breast cancer metastasis. We believe that, with sufficient training data and further parameter tuning, the idea of personalized classifiers may be generalized to other applications, such as predicting clinical endpoints for other cancer types and designing personalized medicine for cancer patients.


M genes and N cell samples. From this matrix, scientists can study how cell protein synthesis changes in response to various factors, for example, disease versus non-disease states in response to a treatment protocol. This technology’s critical challenge is detecting and accurately recording lowly expressed genes. As a result, low expression levels tend to be missed and recorded as zero - an event known as dropout. This makes the lowly expressed genes indistinguishable from true zero expression and different than the low expression present in cells of the same type. This issue makes any subsequent downstream analysis difficult.In recent years, the introduction of single-cell RNA sequencing (scRNA-seq) has enabled the analysis of a cell’s transcriptome at an unprecedented granularity and processing speed. The experimental outcome of applying this technology is a To address this problem, we propose an approach to measure cell similarity using consensus clustering and demonstrate an effective and efficient algorithm that takes advantage of this new similarity measure to impute the most probable dropout events in the scRNA-seq datasets. We demonstrate that our approach exceeds the performance of existing imputation approaches while introducing the least amount of new noise as measured by clustering performance characteristics on datasets with known cell identities.https://github.com/khazum/ccImpute.ccImpute is an effective algorithm to correct for dropout events and thus improve downstream analysis of scRNA-seq data. ccImpute is implemented in R and is available at  In recent years the development of scRNA-seq technology has enabled a more thorough analysis of how transcriptome expression varies among different cells –9 responAs a result, the major challenge with a dataset produced by scRNA-seq is how to deal with a high degree of noise –18. SomeMany current approaches have been proposed to address the issue of dropout events. Zero-Inflated Factor Analysis (ZIFA) adopts a factor analysis (FA) approach for dimensionality reduction to work with a zero-inflated model. Like FA, ZIFA’s data generation process assumes that cell sub-types initially exist as points in a latent (unobserved) low-dimensional space. Next, these points are projected as points in a latent high-dimensional space corresponding to the gene expression matrix using linear transformation and added Gaussian-distributed noise; this allows each measurement to have some probability of being zero using the dropout model that controls the influence of the latent distribution expression values. . Markov This work takes advantage of the effectiveness of consensus clustering as a similarity measure and introduces the ccImpute algorithm that utilizes this similarity measure to impute the dropout events in the scRNA-seq data effectively. We compute the consensus matrix using an approach inspired by the SC3 algorithm . HoweverccImpute relies on a consensus matrix to approximate how likely a given pair of cells is to be clustered together and thus considered to be of the same type. Both the general technique and success of SC3 inspired our approach in generating a consensus matrix. SC3 computes Pearson, Spearman, and Euclidean distance measures between cells followed by linear PCA and non-linear Laplacian dimensionality reductions, resulting in six data transformations. Each of these is further split into subsets, each having a different range of reduced dimensions. Each subset is then clustered using the K-means algorithm, and the results of all runs are then aggregated into a consensus matrix. We have experimentally determined  as the quality of the consensus matrix is improved. Once the consensus matrix is computed, the values are processed to remove noise and prepared for further matrix operations. Dropout More detailed algorithm elements are outlined in the methods section. In the following subsections, we describe the performance characteristics of ccImpute.We tested how well ccImpute can improve the quality of downstream analysis by measuring the difference in how well dimensionality reduction techniques, such as PCA(linear) and t-SNE (non-linear)–both known to be significantly affected by the presence of the dropout events–reduce the data while preserving the data characteristics. We compared the quality of K-means clustering on the reduced data using the Adjusted Rand Index (ARI) against the known labels of the datasets. This allows measuring to what degree imputation affects the simplest task of grouping the like cells. Our ten scRNA-seq datasets are comprised of five publicly available datasets and five synthetically generated datasets. We compared ccImpute with five other state-of-the-art algorithms: MAGIC, DrImpute, scImpute, DCA, and DeepImpute. ccImpute’s imputation resulted in the best improvement of K-means clustering quality as measured by ARI scores on data reduced both by PCA and t-SNE as summarized in Figs. Whether actual or dropout values in scRNA-seq datasets, most of the matrix is populated by zeroes. Thus, intuitively only a fraction of zeroes can be reliably recognized as dropout values and imputed since the estimation of dropout values has to happen given the limited remaining data. Given the hesitancy to use imputation tools in the research community due to introduced bias, we evaluate the impact of imputation algorithms on changing expression values. As shown in Fig. N corresponds to a cell sample size. Since most of the operations in our approach are linear algebra operations involving matrices, and such operations are already optimized in most computing environments due to the presence of BLAS and LAPACK libraries, ccImpute runs much faster in practice. As seen in Fig. ccImpute has Dropout events present a challenge in downstream analysis of the scRNA-seq data. This work presents an imputation method based on the existing success of consensus clustering. We demonstrate that our method deals with the problems of dropout events in scRNA-seq better than any of the state-of-the-art approaches by improving K-means clustering accuracy and like cell separation after linear (PCA) and non-linear dimensionality reductions (t-SNE), both affected by the presence of dropout events. Our technique allows a formal assessment of the degree noise affects clustering assignments. Thus, we can focus on relationships between cells that are not affected much with the presence of a bit of noise. We use this information in the form of a consensus matrix to establish what values are to be imputed by employing weighted voting. This is followed by a weighted mean imputation of the values determined to be dropout events. We demonstrate experimentally that our method not only improves the performance of downstream analysis best, but also does with least bias introduced to the count values. We also include an additional set of results in the Additional file N was computed by dividing raw read counts for each cell by a cell’s total read count, then multiplied by a scale factor of 1, 000, 000, and finally, the log-transformed matrix T was computed using Five experimentally obtained  and five synthetically generated scRNA-seq datasets with details summarized in Table W less than or equal to the ranked outcome f is a function that returns 1 if X and Y, they are plugged into weighted Pearson correlation coefficient formula:As the first step, each dataset was reduced to a d-dimensional representation of I denotes nCenter r is the rank of matrix Compute Singular Value Decomposition of d-dimensional  transformation was computed on weighted Sperman Distance matrix with the variables shifted to be zero centered and scaled to have unit variance. PCA  is a stael trick , 38.m-dimensional data, PCA fits a d-dimensional ellipsoid to the data where principal components are axes of the ellipsoid. Small and long axes represent small and large variances, respectively. The data is reduced by omitting the small axes. PCA performs well when when the data is high dimensional and the variables are correlated. Correlation is an indication of redundancy between the variables. Additionally, PCA is sensitive to the relative scaling of the original variables.Geometrically, given K-means (KM) is the most commonly used clustering algorithm despite its simplicity and almost 60-year lifetime [Clustering , 40 a delifetime , 42. KM k, and a distance metric d and produces a set of centroids k centroids Assign each data datum to nearest centroid: Update centroids The simplest approach to updating centroids is calculating the average of the block. Iteration continues until the set of centroids is stable; in other words, convergence is guaranteed in a finite number of steps by showing that for some non-negative error functionThe KM algorithm takes as input data k, error will be made smaller without any apparent value to the output of the algorithm itself. Given that the algorithm is so simple, finding other limitations in KM is not difficult. For example, while many metrics are available Euclidean distance has historically been the first choice. Recent work showing a more general class of distance, Bregman divergences, has proven useful in signal processing and speech recognition. This allows, under some conditions, a broadening of the data amenable to KM. However, as a corollary to its sensitivity to outliers, when O(nmki), it also requires more discriminatory power–even ignoring the curse of dimensionality, a general problem all algorithms face.The initialization of the starting set of centroids greatly affects KM’s performance. While in theory convergence is assured, achieving this in practice is another matter. Often, the iterate is part of the stopping condition. While relatively easy to understand and implement, the strategy has a significant weakness in that by simply increasing M of the subsets was limited to 15 by selecting every other floor(M/15) sub-dataset. This was followed by KM clustering on each sub-dataset with C is calculated by averaging all similarity matrices. This process is summarized in Fig. We used PCA to transform the weighted Spearman distance matrix, that was further split into several sub-datasets each corresponding to a different number of most significant principal components. Our goal was to produce sub-datasets to measure aggregated KM clustering similarity with respect of varying amount of noise added. For datasets with 500 cells or more, we started with all principal components that were equal or less than 0.01 relative variance and kept adding sub-datasets until reaching the number of principal components with 0.008 relative variance or less. For small datasets, this was done using a specific range following the sequence The pseudocode of the ccImpute is outlined in Algorithm 1. As the first step in lines 2 and 4 the influence of the cell itself and all entries below a threshold value in the consensus matrix are removed to lower the influence of unlikely cell clustering assignments. This threshold value should be lower for higher quality clustering approaches and higher for lower quality approaches. In line 6: the remaining entries are reweighed so that each row values become a probability vector, and the resulting non-valid numbers are replaced with 0 in line 8. In lines 11-14, each cell casts a weighted vote to decide if a given value is a dropout. A cell’s vote magnitude is its consensus value with the cell it is voting on. If the cell’s gene entry value is non-zero, the vote is positive and negative otherwise. If the sum of votes is above zero, the gene count value of a cell is considered a dropout and needs to be imputed. Otherwise, it is left unchanged. Next, the values determined to be dropouts are computed as a weighted mean of its neighbors with weights derived from the consensus matrix. Since some of the dropout values to be imputed depend on other not yet imputed values, this problem is reformulated as a system of linear equations in lines 18-33. In line 35, the values of dropouts are computed using a linear equation solver. This is followed by replacing the dropout values in the original matrix with newly computed values in lines 36-38.To evaluate the performance of the ccImpute algorithm, we compared how well the proposed imputation improves the quality of the PCA/K-means clustering and t-SNE/K-means clustering as compared to other imputation approaches  is:a corresponds to the pair of data points that are in the same cluster in X and Y, b corresponds to pair of data points that are in different clusters in X and Y, c corresponds to pair of data points that are in the same cluster in X but in different clusters in Y, and d corresponds to pair of data points that are in different clusters in X but in the same cluster in Y.Given two clustering assignments Adjusted Rand index (ARI)is adjusted for chance as follows:i in Cluster d being a a distance metric between i and j, and for For each data point All experiments were run on a general-purpose Lenovo NeXtScale nx360 M5 compute node equipped with two 12-core Intel Xeon E5-2680 v3 CPUs and four 480 GB solid-state drives. The number of cores available for computation was set to 16. The R version 4.1.1 was compiled with the Intel compiler version 2021.4.0 using the Intel MKL library to maximize the FLOP performance of matrix operations in our experiments.The SC3 v3.14 package was downloaded from R Bioconductor, and served as foundation to generate the consensus matrix in our approach.The Rtsne v0.15 R package was used with perplexity=30 for most datasets with exception of Blakeley where perplexity=9 was set to account for low number of samples. This was followed by kmeans core function from stats package v3.6.2 in R with algorithm=Hartigan-Wong and options: iter.max=R MAGIC v3.0.0 was used with default settings, and no gene filtering.DrImpute was installed from the Github repository: gongx030/DrImpute. The data was run without gene filtering step.scImpute v0.0.8 was installed from Github repository: Vivianstats/scImpute. The algorithm was run with default settings, and number of cores set to 15.DCA v0.3.1 was installed via preferred installer program in python. The data was imputed with defaults settings.DeepImpute Release 1 was installed via preferred installer program in python. The algorithm was run with default settings.Splatter v1.18.2 was installed from Bioconductor. The data was generated with groups setting, group membership probabilities ,built-in dropout setting applied to all cells, and no batch noise added.Additional file 1. Supplementary Information. A pdf file that contains experimental details and results along with relevant figures and tables omitted from the main paper.


Breast cancer is one of the top three commonly caused cancers worldwide. Triple Negative Breast Cancer (TNBC), a subtype of breast cancer, lacks expression of the oestrogen receptor, progesterone receptor, and HER2. This makes the prognosis poor and earlydetection hard. Therefore, AI based neural models such as Binary Logistic Regression, Multi-Layer Perceptron and Radial Basis Functions were used for differential diagnosis of normal samples and TNBC samples collected from signal intensity data of microarrayexperiment. Genes that were significantly upregulated in TNBC were compared with healthy controls. The MLP model classified TNBC and normal cells with anaccuracy of 93.4%. However, RBF gave 74% accuracy and binary Logistic Regression model showed an accuracyof 90.0% in identifying TNBC cases. Breast cancer is a highly heterogeneous disease and accumulation of distinct malignancies that expresses in the mammary glands. Carcinomas make up the common of breast cancers while sarcomas such as phyllodes tumors and angio-sarcomas are rarely seen.Providing an accurate prognostication for breast cancer patients is significant in order to inform them exactly about the course of the disease and to assign them to the right treatment modality . It is tA comprehensive literature mining (Table 1 - see PDF) of all eligible studies on Breast cancer gene expression was carried out by searching Gene Expression Omnibus (GEO) datasets using the query given below: A1 AND (("B" AND ((H OR h)) A2 AND (("B" AND ((H ORh)) A3 AND (("B" AND ((H OR h)) Where, A1 = Gene Expression; A2= Expression array; A3= Microarray; B= Breast cancer; H= Homo sapiens; h=human The concept lexicon was limited to Homo sapiens so as to retrieve only datasets containing studies or data pertained tohuman beings.Gene expression profiling is a method to measure the expression levels of thousands of genes simultaneously and sometimes, even an entire genome. This can yield vital information on the functions and activities of the gene of our interest. Pre-processeddatasets were chosen by systematic text mining technique as described above. GEO2R was used for the gene expression profiling analysis of the chosen dataset. Based on the mining, microarray datasets were retrieved from NCBI. GEO repository using accession numberGSE45498 annotated in GPL16299 platform. The dataset comprises of 40 healthy normal samples, 160 with cancer, 54 meta-static samples. The gene expression profiling values were log (base2) transformed and percentage shift normalization was performed. The foldchange differences in gene expression between normal and disease samples were calculated for each gene separately. A cut off value of 1.25 fold change was used to classify up regulated genes [Machine Learning is a branch of Artificial Intelligence (AI) that employs a variety of statistical, probabilistic and optimization techniques that allows computers to "learn" from past examples and to detect hard-to-discern patterns from large, noisy orcomplex data sets . These sLogistic regression is one of the Machine Learning algorithms, which comes under the Supervised Learning technique. It is a predictive analysis algorithm that predicts a categorical dependent data variable by analysing the independent variables that arepresent. The most commonly used model is binary logistic regression model. When dealing with multiple genetic factors and other covariates, logistic regression assumes a linear relationship among the predictors and uses a logit link to combine them into aone-dimensional fitted value .Artificial neural networks (ANNs) are statistical models which are able to abstract patterns in the observed data without the need for moulds about the relationships between the numerous variables . ANNs arMLP is the most utilized model in neural network applications using the backpropagation training algorithm . An MLP RBF networks uses Euclidean distances and Gaussian activation functions, which makes neurons more locally complex. RBF has two layers; the first layer is radial basis and the output layer is linear. RBF has used SoftMax as an activation method. Trainingprocess is done by competitive learning or clustering. Network performance can be improved by changing these parameters. By applying inputs to the network, the distance between input vectors and weight vectors is calculated and vector product is obtained bymultiplying the calculated values by bias values. Then, these values generate as many neurons as inputs by corresponding functions; finally, output values are obtained by output layer . In thisTo predict the goodness of values by the developed MLP and RBF models, the Receiver Operating Curve (ROC) was employed. ROC is a graphical display of sensitivity (True positive results (TPR) on y-axis) and (1 - specificity) and on x-axis) for fluctuating cut-off points of tested values ranged from 0 to 1. The Area under the Curve (AUC) is an effective and combined measure of sensitivity and specificity for assessing inherent validity of a diagnostic test. Maximum AUC = 1 and it showsthat the diagnostic test is perfect in differentiating disease with non-disease subjects. This infers both sensitivity and specificity are one and both errors - false positive and false negative–are zero. This can happen when the distribution of disease andnon-disease test values does not overlap. This is extremely unlikely to happen in practice. The AUC closer to 1 indicates better performance of the test. The diagonal connecting the point  to  divides the square into two equal parts and each has anarea of 0.5. When ROC is this line, overall, there is 50-50 chances that test will correctly discriminate the disease and non-disease subjects. The minimum value of AUC should be considered 0.5 instead of 0 because AUC = 0 means the test incorrectly classifiedall subjects with disease as negative and all non-disease subjects as positive. If the test results are reversed, then area = 0 is transformed to area = 1 .Cancer treatment has progressed substantially over the past years with a reduction in therapy intensity, both for loco regional and systemic therapy; avoiding over treatment but also under treatment has become a major focus .There iAfter 13 iterations of Binary Logistic Regression, the genes EGR1 and PPARG were identified to be significant (p<0.05). Binary logistic regression model thus generated showed net accuracy of 90.0%. The derived logistic equation is as follows:Y= EGR1 * (-0.03) + PPARG * (- 0.025) + 3.63Upregulated 15 genes were taken as input layer and the category as output layer. All data were standardized, 70% of data was allocated as training set and the remaining 30% as test set. MLP was built with one hidden layer, the resultant neural network wasfound to be capable of classifying TNBC cases with 94.9% accuracy and controls with 88.2% of accuracy. The overall accuracy of MLP was found to be 93.4% (Table 3 - see PDF) Radial basis function network was found to be capable of classifying TNBC cases with97.1%accuracy and controls with 20% of accuracy and it was found to be capable of classifying with an accuracy of 74.0%. MLP was found to perform better than RBF and binary logistic regression (Table 3 & 4 - see PDF). To compare two different classificationmodels, AUC was calculated  for the two models and ROC was constructed. The high AUC value is connected to high precision rate. In ROC space X-axis is Specificity and Y-axis is sensitivity. At the standardized specific threshold, themodel outputs specificity (94.9%) and sensitivity (93.4%), to draw a point in ROC space. All the point of Normal and Triple negative breast cancer joins into ROC curve. The ROC curve revealed highly significant classifying ability among the disease diagnosis. The relWe describe a novel machine learning model to differentially diagnose TNBC from normal samples. We have demonstrated the properties and advantages of the model using TNBC gene expression dataset. We have also presented the performance of Machine layertechniques such as Artificial Neural Network and regression analysis model. The performance of classification algorithms is usually examined by evaluating the accuracy of the classification. We used three machine learning models namely, MLP, RBF and Binarylogistic regression model for the dataset. The overall classification accuracy has been presented in Table 5(see PDF). The MLP model shows an accuracy of 93.4% which produced better specificity and accuracy compared to the RBF (74% of accuracy) and the binarylogistic regression model (90% of accuracy). Data shows that the order of effectiveness in diagnosis of different neural networks is MLP followed by RBF. Data also shows the strong ability of neural networks with better performance. These networks storeprototypes that are looked up for deciding the network output. Data shows that good performance is given by MLP followed by RBF. These networks try to predict the decision boundaries in the form of curves. The higher performance of the MLP as compared to theRBF in the testing data is an indication of a higher generalizing capability of the MLP. RBF gave a reasonably poor performance for the dataset. This indicates the limitation of the recurrent architecture to solve the classification problems due to theirhighly localized nature. The efficacy of the machine learning models in different populations is to be validated.


Cancer of unknown primary (CUP) refers to cancer with primary lesion unidentifiable by regular pathological and clinical diagnostic methods. This kind of cancer is extremely difficult to treat, and patients with CUP usually have a very short survival time. Recent studies have suggested that cancer treatment targeting primary lesion will significantly improve the survival of CUP patients. Thus, it is critical to develop accurate yet fast methods to infer the tissue-of-origin (TOO) of CUP. In the past years, there are a few computational methods to infer TOO based on single omics data like gene expression, methylation, somatic mutation, and so on. However, the metastasis of tumor involves the interaction of multiple levels of biological molecules. In this study, we developed a novel computational method to predict TOO of CUP patients by explicitly integrating expression quantitative trait loci (eQTL) into an XGBoost classification model. We trained our model with The Cancer Genome Atlas (TCGA) data involving over 7,000 samples across 20 types of solid tumors. In the 10-fold cross-validation, the prediction accuracy of the model with eQTL was over 0.96, better than that without eQTL. In addition, we also tested our model in an independent data downloaded from Gene Expression Omnibus (GEO) consisting of 87 samples across 4 cancer types. The model also achieved an f1-score of 0.7–1 depending on different cancer types. In summary, eQTL was an important information in inferring cancer TOO and the model might be applied in clinical routine test for CUP patients in the future. This kind of cancer is called cancer of unknown primary (CUP), which is commonly treated by broad-spectrum chemotherapy with a usually bad prognosis. A landmark study suggested that therapy targeting primary lesion could significantly improve the survival of patients  is a locus that explains the association between SNPs and gene expression levels . eQTL anhttps://tcga-data.nci.nih.gov/tcga/) and GTEx analysis . Based on the eQTLs, we trained a CUP model using data from TCGA. We validated the performance of our model by cross-validation and independent testing through our collected data from Gene Expression Omnibus (GEO).Though eQTL has been widely used in cancer research, it has not been applied in CUP analysis. In this study, we integrated eQTL into our machine learning model to infer the primary lesion of CUP. Specifically, we first collected cancer-associated eQTLs based on The Cancer Genome Atlas (TCGA) data portal  to support searching, browsing, and downloading. We downloaded cis-eQTLs for 20 cancers, which have been studied abundantly and have more complete data samples, from PancanQTL for further study.In order to obtain cancer-related eQTL, the calculation can be carried out according to the process mentioned in the The training data were downloaded from TCGA, and the test data were downloaded from GEO. The number and proportion of samples for each cancer in the training data and test data are detailed in https://docs.gdc.cancer.gov/Data/File_Formats/MAF_Format/.Due to the fact that the input files for the next step, “quality control with Plink”, need to be in MAP and PED formats, the raw TCGA data must be converted into MAP and PED files. There are 7 columns of data in the PED file, and the names of each column are as follows: Family ID , Individual ID, Paternal ID (0 = unknown), Maternal ID (0 = unknown), Phenotype (0 = unknown), sex , and SNP type data. There are 4 columns in the MAP file, and the data names of each column are as follows: chromosome number , SNP name , molar position of chromosome , and SNP physical coordinate (position of variant on chromosome). The MAP content can be defined using the following website: In this step, confounders are corrected and normalized. A confounder can be any unknown variable that affects the correlation measure between the independent and dependent variables (genetic and non-genetic bias) . Its purhttp://zzz.bwh.harvard.edu/plink/) or synbree (https://cnsgenomics.com/software/gcta/#Overview) to generate the top 3 PCs.1. The first three genotyping principal components (PCs): Firstly, we can do quality control analysis with Plink  to generate 15 PEER factors.2. The first 15 expression PEER  factors: In this step, we can use PEER Programs  is used for ultra-fast analysis  /N .Noise interference is randomly added to the features of all samples of OOB data outside the bag , and the error of data outside the bag is calculated again, which is denoted as errOOB2 increases), which indicates that this feature has a great influence on the prediction result of the sample, and thus the importance is relatively high.The reason why this value can explain the importance of the feature is that, if the random noise is added, the accuracy of the OOB data decreases significantly  for cancer classification. The multilayer perceptron classifier of Kurt Hornik et al. in 1989 was based on the feedforward artificial neural network (ANN) classifier . FeedforFrom input layer to hidden layer: Since input layer X={1, x1… ,  xm}  to the hidden layer A={1,  a1, … , ak} is fully connected, where element 1 is the bias node, then the output of the hidden layer is  X1= f1( W1X+ b1), where W1 is the weight ; b1 is offset. The f function can be the usual sigmoid or tanh function 3:From hidden layer to output layer: Hidden layer to output layer is a multi-category logistic regression, namely, Softmax regression; thus, the output of the output layer is  f2( W2 X1+ b2), where f2 is Softmax function 4.ix is the output value of the ith node and J is the number of output nodes. Obviously, the Softmax function can limit the output value conversion range of multiple classification problems to , and the sum is 1.where Neural networks have the remarkable ability to make meaning out of complex or imprecise data, and can be used to extract patterns and detect complex trends that neither humans nor other computer technologies can notice. A trained neural network can provide a prediction. Its advantages include the following: MLP is self-adaptive; MLP does not make any comparisons with other probability-based models of functions or other probability-based information considered in its assumptions about potential probability density; and the required decision-making function can be generated directly through training.The eQTLs of 7,000 samples across 20 types of solid tumors were downloaded from PancanQTL. The genes where these eQTLs were located intersected with the genes in the training data. Following the intersection, the random forest algorithm was used to select the features of these genes, and XGBoost and MLP Classifier were used to classify them. The TCGA data were randomly divided 9:1 and 1/10 was used for testing and 9/10 were used for cross-validation   . KLK2 ise cancer . STEAP2,e cancer .The top 800 genes that made the testing data the most accurate were selected for enrichment analysis with the Gene Ontology (GO) database and the Kyoto Encyclopedia of Gene and Genomes (KEGG) database by Metascape. The results indicated that these genes were significantly enriched in pathways in cancer, especially in gastric cancer and basal cell carcinoma . The patThe results of GO enrichment analysis . LymphocCUP is a malignant cancer with a high mortality rate. The study of CUP from the perspective of gene expression and SNP is conducive to the fundamental understanding of the disease and the improvement of treatment.In previous studies, eQTL has shown tissue specificity . eQTL isIn this model, eQTL is applied to infer tumor origin for the first time, which achieved better performances than using single markers. However, there are a few limitations of this study. Firstly, previous studies suggested that other biomarkers like pathological images are important in cancer diagnosis and prognosis prediction –53. It wIn this study, we first described the biological basis of eQTL and the commonly used mathematical models, then we discussed the application of eQTL in diseases and cancer, as well as the general use of eQTL in cancer analysis and other software and websites for additional information. We used eQTL to classify cancer. The results of 10-fold cross-validation of TCGA data with different features led to the selection of XGBoost as the optimal model, and the reason for this selection is explained along with its eQTL. Afterward, we discussed the possibility of using other algorithms in eQTL analysis to solve the problems in traditional analysis, and also discussed the use of eQTL analysis for subjects other than mRNA expression.The original contributions presented in the study are included in the article/supplementary material. Further inquiries can be directed to the corresponding authors.Ethical review and approval was not required for the study on human participants in accordance with the local legislation and institutional requirements. Written informed consent for participation was not required for this study in accordance with the national legislation and the institutional requirements.KL and GT conceived the project; YM and XZ implemented the experiments and wrote the manuscript; WZ and DX collected data; DX, XS, SC, JL, JT, and XY analyzed the data and revised the manuscript; all authors approved the final manuscript.Authors GT and XS were employed by Geneis Beijing Co. Ltd and Qingdao Geneis Institute of Big Data Mining and Precision Medicine. XY was employed by Geneis Beijing Co. Ltd.The remaining 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.


Cancer is one of the top causes of death globally. Recently, microarray gene expression data has been used to aid in cancer’s effective and early detection. The use of DNA microarray technology to uncover information from the expression levels of thousands of genes has enormous promise. The DNA microarray technique can determine the levels of thousands of genes simultaneously in a single experiment. The analysis of gene expression is critical in many disciplines of biological study to obtain the necessary information. This study analyses all the research studies focused on optimizing gene selection for cancer detection using artificial intelligence. One of the most challenging issues is figuring out how to extract meaningful information from massive databases. Deep Learning architectures have performed efficiently in numerous sectors and are used to diagnose many other chronic diseases and to assist physicians in making medical decisions. In this study, we have evaluated the results of different optimizers on a RNA sequence dataset. The Deep learning algorithm proposed in the study classifies five different forms of cancer, including kidney renal clear cell carcinoma (KIRC), Breast Invasive Carcinoma (BRCA), lung adenocarcinoma (LUAD), Prostate Adenocarcinoma (PRAD) and Colon Adenocarcinoma (COAD). The performance of different optimizers like Stochastic gradient descent (SGD), Root Mean Squared Propagation (RMSProp), Adaptive Gradient Optimizer (AdaGrad), and Adaptive Momentum (AdaM). The experimental results gathered on the dataset affirm that AdaGrad and Adam. Also, the performance analysis has been done using different learning rates and decay rates. This study discusses current advancements in deep learning-based gene expression data analysis using optimized feature selection methods. Cancer is one of the deadliest diseases, and with its increasing prevalence, early identification and treatment are critical  Schiff . Lung caAbout two-third of cases are detected at initial stages . The claThe selected articles for analysis have been published in last 5-years. Most of the research articles explored in this study have been published in 2018 and 2019. The articles that have explored gene expression data for cancer diagnosis/survival/stage prediction have been included in this study. • This article reviews recent developments in deep learning-based feature selection techniques for gene expression data interpretation and offers an extensive review of Deep Learning architectures that have demonstrated success across a wide range of industries and are now used to help doctors identify various chronic conditions.• In this work, we have compared the outcomes of several optimizers on a dataset of RNA sequences. The study’s deep learning system categorizes five types of cancer: colon cancer, lung adenocarcinoma, prostate cancer, invasive breast carcinoma, and kidney clear cell carcinoma (COAD).• The efficiency of several optimizers, including adaptive gradient optimization (AdaGrad), stochastic gradient descent (SGD), root mean square propagation (RMSProp), as well as adaptive momentum (Adam). AdaGrad and Adam are more precise, according to the experimental findings discovered in the dataset. The performance of a variety of learning and decay rates was explored in the performance study.The study contributes in a number of ways. Following are the significant contributions made by the study:This paper is organized in a way that boosts the comprehensibility of the article. Second section gives the description of the significance of gene-expression analysis in cancer research. The search strategy used in this paper is Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) strategy. All the research studies selected for this systematic review have been extracted from databases like PubMed, Web of Science, EBSCO, and EMBASE. All the research articles that have been published before 2016 are excluded from the analysis. The keywords used for extraction of articles include “Deep Learning”, “Artificial Intelligence”, “Cancer”, “Micro-array analysis”, “gene-expression”, and combination of these keywords. The research articles that have focused on the optimization of gene selection using deep learning techniques have been included in the study. The Artificial intelligence is the idea of making innovative and intelligent machines. Machine learning is an artificial intelligence subset that aids in developing AI-driven applications. Deep learning is a subtype of machine learning that trains a model using large amounts of data and advanced methods. The significant differences between deep learning approaches and traditional learning are summarized in • Artificial Neural Networks: One of the most often used data modeling algorithms in medicine is neural networks. In the early 20th century, neural networks were developed . The priSimilarly, a neural network represents multiple neurons with a weight assigned to each link. These neurons act in parallel. During the learning stage, the network updates the weights for prediction of proper input to produce the output function . DiffereTanh) optimization technique is given in .The mathematical working of Hyperbolic Tangent (Relu) optimization technique is expressed in .The working of Rectilinear Unit : CNN is a multi-layer neural network based on the visual brain of animals. LeCun et al. constructed the first CNN. CNN’s major application areas include image processing and character recognition . In termBecause of its adaptive character, altering the weights aids in the minimization of error. In contrast to basic modeling methods, neural networks have the advantage of predicting non-linear relationships. In the study of medical data, neural networks play a significant role such as medication development. The use of a neural network to predict cardiac disease is possible.The cell’s weight can be utilized as a regulating factor. There is a requirement for a training approach known as Backpropagation through time (BPTT) that improves weight. For optimization, the technique requires network output error.Deep learning has been widely utilized to improve prognosis . Gene exProcessing microarray gene expression data is a diverse field of computer science that includes graph analysis, machine learning, clustering, and classification. Microarray technology allows for the measurement of thousands of gene expressions in a single experiment. Gene expression levels aid in identifying linked genes and disease development, which aids in the early detection and prognosis of many forms of cancer.Using microarray gene expression patterns , developThe experimentation with colon and leukemia data demonstrates the proposed cancer classification’s efficacy. The accuracy rate of the proposed classification approach employing gene expression data is 0.9534, and 0.9666 detection rate.Despite decades of research, clinical diagnosis of cancer and the identification of tumor-specific markers remain unknown . offeredIn the above equation, t values . appliedt values . tested A retrospective study  investigated the use of Deep Learning (DL) to predict acute myeloid leukemia (AML) prognosis. This study used 94 AML cases from the TCGA database. Age, ten common cytogenetic mutations, and the 23 most common mutations have been used as input data. Also, the results suggested feasible applications of deep learning (DL) in the prognostic prediction utilizing next-generation sequencing (NGS) data as proof-of-concept research.Research work  proposedth instance and The term  employedHere a Nonetheless, it is characterized by highly high-dimensional data, which results in an over-fitting problem for the classifying model . purposeSignificant bioinformatics research  has been discoverRMSE). The mathematical formula of RMSE is given in  proposedMultiple studies aimed to investigate cancer prediction models. This section holds the simulation results achieved using ANN model along with multiple optimizers like Stochastic gradient descent (SGD), Root Mean Squared Propagation (RMSProp), Adaptive Gradient Optimizer (AdaGrad), and Adaptive Momentum (AdaM). Also, the performance analysis has been done using different learning rates and decay rates.https://archive.ics.uci.edu/ml/datasets/gene + expression + cancer + RNA-Seq. This dataset comprises data on five different forms of cancer, including kidney renal clear cell carcinoma (KIRC), Breast Invasive Carcinoma (BRCA), lung adenocarcinoma (LUAD), Prostate Adenocarcinoma (PRAD) and Colon Adenocarcinoma (COAD). The dataset consists of 20,531 attributes of 801 patients.TCGA dataset is available at The performance of multiple optimizers is analyzed and shown in The ANN model using SGD and rmsprop optimizer attained 35.3% on training data and 43.8% on test data. Both the Adam approaches performed well. Hence, we considered analyzing the performance of different parameters like learning rates and decay rates.ADAM optimizer using different learning rates is analyzed and shown in The performance of −05) performed the best on training and testing data. The ANN models performed worst (35% on train and 43.8% on test set) with slowest .From the figure, it is clear that learning rate  is used to train current neural networks. It begins with a high rate of learning and then decays several times. It has been demonstrated empirically to aid in both optimization and generalization. The performance of From the figure, it is clear that decay rate  performed the best on training and testing data. The ANN models performed worst (35.3% on train and 43.8% on test set) and (63.5% on train and 68.7% on test set) with decay rates “0.01” and “0.0001” respectively.Several strategies for gene selection in cancer categorization have been proposed in prior studies. The advent of deep learning has profoundly affected a wide variety of machine learning applications and research. Few of such studies , Gupta , Gupta  are descInitially, the exploration of data is done and termed as “exploratory data analysis”. Further, data preprocessing steps are used like cleaning data, reducing dimension (feature reduction), normalizing the data. Further the next stage splits the preprocessed data into sets. The deep learning classification algorithm is trained on the training set for classification of data. The trained classification model is further evaluated on the test set. The evaluation of the data can express the accurateness of the model. The number of cancer cases is rapidly increasing. It is difficult to diagnose because the illness is frequently asymptomatic in its early stages. Early detection can increase the odds of a patient’s recovery and cure. Cancer is notoriously difficult to diagnose in its early stages and is prone to recurrence after treatment. Cancer classification is a crucial topic. One of the most effective methods for cancer classification is gene selection . The tasSeveral academics have investigated automated learning methodologies; however, these approaches still have several flaws that make cancer classification difficult. Specific machine learning algorithms have been found incapable of exploiting unstructured data in cancer classification. CNNs are particularly appropriate for analyzing a wide range of unstructured data. This capability enabled deep learning algorithms to take an active role in the early diagnosis of cancer through data classification. Deep learning approaches have achieved high accuracy and other statistical characteristics. Deep Learning has succeeded in various domains, including image, video, audio, and text processing. Deep Learning faces a unique problem in gene expression analysis for various cancer detection and prediction tasks to define appropriate biomarkers for different cancer subtypes. Despite several research studies on multimodal treatment approaches, survival times remain short. The gathering of significant genes that can increase accuracy can provide adequate guidance in early cancer detection. Cancer can be classified into several subgroups. However, it is a complex task because of the vast number of genes and the comparatively few experiments in gene expression data . Cancer Cancer has become one of the top causes of death worldwide in recent years. As a result, increasing research is being done to determine the most effective diagnosing and treating cancer. However, cancer treatment faces numerous obstacles, as possible causes of cancer include genetic problems or epigenetic modifications in the cells. RNA sequencing is a substantial approach for assessing gene expression in model organisms and can provide information for bio-molecular cancer diagnosis. Microarray gene expression profiles can be used to classify tumors efficiently and effectively. Predicting various tumors is a significant problem, and offering accurate predictions would be highly beneficial in delivering better therapy to patients. The advent of deep learning approaches is critical for improving patient monitoring, as it can aid clinicians in making decisions regarding deadly diseases. Furthermore, Gene expression data are utilized to develop a classification model that will help cancer treatment. Classification of cancer subtypes is critical for effective diagnosis and individualized cancer treatment. The article concludes that the recent advances in high-throughput sequencing technology have resulted in the quick generation of multi-omics data from the same cancer sample. Thus, deep learning-based molecular illness classification holds considerable promise in the realm of genomics, particularly concerning gene microarray data.


The increased incidence and the significant health burden associated with Parkinson’s disease (PD) have stimulated substantial research efforts towards the identification of effective treatments and diagnostic procedures. Despite technological advancements, a cure is still not available and PD is often diagnosed a long time after onset when irreversible damage has already occurred. Blood transcriptomics represents a potentially disruptive technology for the early diagnosis of PD. We used transcriptome data from the PPMI study, a large cohort study with early PD subjects and age matched controls (HC), to perform the classification of PD vs. HC in around 550 samples. Using a nested feature selection procedure based on Random Forests and XGBoost we reached an AUC of 72% and found 493 candidate genes. We further discussed the importance of the selected genes through a functional analysis based on GOs and KEGG pathways. Parkinson’s disease (PD) is a chronic, degenerative disease of the central nervous system with a pattern of incidence that increases with age; as the population ages, its burden is poised to increase . DespiteMassively parallel analysis of cellular RNAs can provide an unbiased set of biomarkers of PD and can generate hypotheses about disease mechanisms. It may be particularly useful for decoding a disease with considerable environmental and epigenetic contributions not readily explained by variations in the genomic fingerprint such as PD . Brain tAs high throughput technologies such as transcriptome sequencing can now generate huge amounts of biological data at relatively low costs, the processing and extraction of relevant signal requires the adoption of artificial intelligence methodologies. A number of Machine Learning (ML) approaches have been undertaken for PD classification that use as input vocal and gait  or neuroFor blood transcriptomics, experience suggests that large cohorts are needed, and that drug-naive patients should be used, as medications certainly affect gene expression . Microarwww.ppmi-info.org/data accessed on 11 March 2022) [Given the importance of using large cohorts of drug-naive patients, we used open access gene expression data from the Parkinson’s Progression Markers Initiative (PPMI), an international study that has enrolled the largest to date cohort of untreated PD patients (around 430 subjects) across multiple sites (ch 2022) .We downloaded PPMI whole blood transcriptome data from the LONI Image and Data Archive (IDA) (data dowloaded in July 2021). From the available set of sequenced samples, we selected 579 samples collected from different individuals, namely 390 subjects in the early PD cohort and 189 age-matched subjects in the HC cohort. Therefore the dataset consisted of twice as many PD cases as HCs. Each sample had expression values (read counts) for a total of around 60,000 transcripts. The early PD cohort included subjects with PD that were not treated with dopaminergic medications, that were not carriers of ‘LRRK2’, ‘GBA’ or ‘SNCA’ mutations, and that did not have a first relative with one or more mutations. Sequence data had been aligned to GRCh37(hs37d5) by STAR (v2.4K)  using exSubject metadata that we downloaded from the PPMI website included biological variables such as age, sex, clinical site and clinical measures of motor symptoms such as indicators of tremor dominant (TD) or postural instability gait difficulty (PIGD), of non motor symptoms such as categorical REM sleep behavior disorder (RBD), of cognitive impairment (CI) such as the Montreal Cognitive Assessment or MoCA index (adjusted for education), and of olfactory function (UPSIT or University of Pennsylvania Smell Identification Test score). Additional metadata included technical variables such as, for instance, RIN (RNA integrity number), percent usable bases, total number of reads, sequencing plate. www.ppmi-info.org accessed on 11 March 2022.For up-to-date information on the study and for access to the data, visit Our computational workflow consists of three main phases: (i) a first preprocessing phase, which was essential to manage the informative content of highly heterogeneous and computationally demanding data such as transcriptomes; (ii) a second learning phase, which exploited a feature importance evaluation embedded in a Random Forest (RF) classification procedure ,26 and wFor all analyses we used R version 4.0.3, packages xgboost v1.6.0.1, caret v6.0-90, and Bioconductor packages DESeq2 v1.30.1, limma v4.46.0, enrichR v3.0, AnnotationDbi v1.52.0, and org.Hs.eg.db v3.12.0. The code used to conduct this research is available upon request.The first phase of our workflow consists of multiple preprocessing procedures. This phase is essential given the large number of features, namely gene expression values based on the GENCODE v19 comprehensive annotation. A number of label independent filtering steps, where the labels are “PD” and “HC”, were required to extract informative content.First, we selected only transcripts corresponding to protein coding genes and long intergenic non-coding RNAs (lincRNAs). Second, we discarded 2667 transcripts driving technical variance , which lAfterwards, we used control samples to estimate the batch effect of the site, that we subsequently removed in both controls and cases  using liAfter this step, we were left with a total of 548 samples. Then we removed further confounding effects due to sex and RIN value, again with limma. Thus, for the subsequent analyses we considered a database including 548 subjects described by 12,612 genes.p-value. Genes with adjusted p-value < 0.05 are called significantly differentially expressed in the two classes. We will evaluate the fold change of genes with its associated error and adjusted p-value and compare results with a multivariate analysis that uses Machine Learning algorithms.Before moving to the second phase of our workflow, namely the learning phase (see next section), we performed differential expression (DE) analysis, which is a classical and univariate approach towards the identification of biomarkers from RNASeq data. We will also test the performance of our ML approach (XGBoost) when we used as input the set of DE genes obtained with DESeq2 instead of the set of genes selected with RF. In the discussion we will contrast the results of this univariate approach with results from our machine learning multivariate approach. For DE analysis we used DESeq2 , a populAfter performing DE analysis, we moved to the second phase of our workflow, the learning phase.Our filtering procedure described in f being the overall number of genes, which is the default value for this parameter. As already mentioned, another important advantage of the RF classifier is its embedded feature importance evaluation; during the training phase, the algorithm can assess how much each feature decreases the impurity of a tree, or the likelihood of incorrect classification of a new instance of a random variable and then can make an average over all trees [C of the most important features was used to train an XGBoost model; the XGBoost classification performance was evaluated on the validation set, for the twenty 10-fold cross validation iterations, in order to obtain an unbiased performance evaluation. As with RF, the XGBoost algorithm belongs to the set of learning approaches called ensemble, which combines and manages the predictions of several weak models to obtain a more robust model. While RF relies on bagging (Bootstrap aggregation), XGBoost exploits the Gradient Boosting framework. In the Gradient Boosting method, new models are applied to predicting residuals or errors of previous models and then added together to obtain the final predictive model. This approach implements a gradient descent algorithm to minimize the loss when including new models [Within a repeated stratified (to tackle the control-patient mismatch) 10-fold cross-validation framework (20 iterations), we trained multiple RF models  to evaluate permutation feature importance measures. We chose RF for two main reasons: on the one hand, RF is easy to tune as it only depends on two parameters, namely the number of trees to grow and the number of features randomly selected at each split; on the other hand, RF is an extremely efficient algorithm on high dimensional data. Each forest was grown using 1000 trees, a sufficient value to allow the algorithm to reach a stable plateau of the out-of-bag internal error. The features selected at each split were ll trees . Using tw models .Overall, our procedure is very robust because, in addition to the high number of iterations implemented, we also use two different classification algorithms in the training and test phases which makes the results independent from the model. Then, to compare the performance of the ML approach to the performance of a simpler XGBoost classification algorithm that uses as input features the set of DE genes obtained with DESeq2, we trained the algorithm on 90% of the data and tested it on 10%.Finally, we tested if the predicted probability of the algorithm was different between PD subjects with different endo-phenotypes: (i) MoCA ≤ 26 and MoCA higher than 26; (ii) PDs with RBD an PDs without RBD; (iii) PDs with TD and PDs with PIGD or undetermined; (iv) PDs with Normosmia and PDs with Hyposmia or Anosmia; (v) PD subjects belonging to different age categories, namely age ≥ 56 and age < 56.The last phase of our workflow is performance evaluation. A binary classification problem has only two class labels; therefore, the resulting model decisions can fall into four categories: true positives (TP) when the model correctly predicts the positive class, erroneous positive predictions  and, analogously, true negatives (TN) and false negatives (FN).AccuracySensitivitySpecificityBalanced AccuracyF1Area Under the Receiver Operating Characteristics (ROC) Curve (AUC), which plots sensitivity against specificity by varying the decision threshold.Given these four cases, one can define several metrics; in particular, we considered here :AccuracySensitivity and specificity evaluate how well the model performs on the positive and the negative class, respectively. The other metrics provide an overall performance evaluation. Although these “overall” metrics are roughly equivalent, their values can ease the comparison of our results with the state-of-the-art.p-value less than 0.05. Of the DE genes, however, only one gene, namely ‘RAP1GAP’, had a log fold change (lfc) higher than 0.5 in absolute value  used to learn the model, this analysis shows that over an extremely broad range of features the informative content remains stable and accurate. For what concerns the other classification metrics obtained using the previously mentioned 493 features, a detailed overview is presented in The model is generally accurate, as shown by “global” metrics ; it is worth noting the performance drop revealed by the balanced accuracy, which reflects the data imbalance. The same consideration holds for the performance gap in terms of sensitivity and specificity.We tested the performance of an XGBoost classification algorithm that used as input features the set of DE genes obtained with DESeq2. We obtained an AUC of 64%, which is considerably lower than the performance of our ML approach based on RF and XGBoost, which proves how a multivariate ML model can be more effective on this type of data compared to classical DE approaches.p-value 0.004, Wilcoxon test, average predicted probability = 0.77 for age < 56 and 0.84 for age ≥ 56), while there was no significant difference between PD subjects belonging to the other considered endo-phenotypic classes.A final note on the performance of the algorithm with respect to PD endo-phenotypes. The predicted probability of the algorithm was higher for PD subjects in different age categories: the algorithm had an average predicted probability higher for PD individuals with age ≥ 56 , we designed an experiment to investigate which were the most important genes for classification. Provided that the highest performance value was obtained with 493 features, within the cross-validation scheme, we evaluated the probability that an input feature (gene) is one of the top 493 genes, see Among the most frequently selected genes, the 20 most important genes (according to the average importance ranking) are listed in We performed KEGG (Kyoto Encyclopedia of Genes and Genomes) pathway and GO (Gene Ontology) functional annotation enrichment analysis with respect to biological processes, cellular components and molecular functions using enrichR  on the lC, and reached a maximum at a number of features C = 493, then slowly decreased. This behavior showed that the informative content of the selected genes was stable and accurate. While there was an imbalance between sensitivity and specificity, it was moderate and, if needed, this discrepancy could be mitigated with additional under-sampling of over-sampling techniques that could be embedded in the described methodology.With the robust methodology we implemented, we identified a set of around 500 genes that could discriminate between PD and HC with an AUC of 72%. Over 20 runs of cross validation  the AUC Comparing our performance with the state-of-the-art is not straightforward because of the nature of the data and because of ongoing research in the area. A comparable study on a large cohort of 523 individuals performed on blood microarray gene expression data and using Support Vector Machines reports an AUC of 79% on the validation set and of 74% on the test set . Howeverp-value < 0.01) on the training set as input features to a Support Vector Machine classification algorithm. Using only transcriptome data they reached an AUC of 79.73% on the validation set, 73.89% accuracy, 54.60% balanced accuracy, 97% sensitivity, and 12% specificity. When they combined transcriptomics with the other multi-modal data using a union of the features as input features; after tuning, they reached an AUC of 85.03%, 75% accuracy, 68.09% balanced accuracy, 93% sensitivity and 43% specificity on the independent test set and determined, by comparing the relative importances of the input features, that the UPSIT score, as well as PRS, contributed most to the predictive power of the model, but the accuracy of these were supplemented by many smaller effect transcripts and risk SNPs.A multivariate study not yet published  and perfThe strength of our work is its high balanced accuracy in delineating cases and controls and its robustness. Our feature selection procedure identified a robust set of around 500 genes listed in Accurate characterization of the selected genes and of significantly enriched gene sets is beyond the scope of this paper; however, we report a few comments on the enrichment analysis and a few notes on the selected genes.Oxidative stress plays an important role in the degeneration of dopaminergic neurons [Inflammation is another important contributor to the pathogenesis of the disease [Mitochondrial dysfunction is another pathway that has been implicated in the pathophysiology of PD through both environmental exposure and genetic factors. The discovery of the role of the PD familial genes ’PTEN’-induced putative kinase 1 (PINK1) and parkin (PRKN) in mediating mitochondrial degradation reaffirmed the importance of this process in PD aetiology [Vesicular dysfunction is another known contributor of PD [diabetes mellitus and inflammatory bowel diseases (IBD) are known PD risk factors. In fact, population-based cohort studies indicate that diabetes and IBD are associated with increased PD risk by about 38% [Our analyses revealed a number of significant functions and pathways, some of which have already been linked to the pathogenesis of PD, such as oxidative stress, inflammation, mitochondrial and vesicular dysfunction, as well as associations between PD and diseases such as diabetes mellitus or inflammatory bowel disease (IBD) see . Oxidati neurons ; its inv neurons . Glutath neurons . Inflamm disease . Interes disease . Neurona disease ,40. The  disease . Mitochoetiology . Vesiculor of PD . Finallybout 38%  and 22% bout 38% , respectA few notes on the set of genes selected follow. In ‘MYOM1’, Myomesin1, the most important gene, is a protein coding gene and is up-regulated in PD subjects. Noticeably, ‘ENSG00000272688’ (Lnc-MYOM1-4) falls within an intron of MYOM1 and is the fifth most important lincRNA; gene ‘MYOM2’ is also in the list of selected genes and was selected in all the 20 repeated runs. Gene ‘MYOM1’ is significantly up-regulated in human substantia nigra pars compacta from PD patients  and is aGene ‘RAP1GAP’ has been identified by both the DE analysis and the ML methodology (it is selected 20 times over 20 repetitions) see . This geTwo final comments. First, the performance of a classification algorithm that used as input features DE genes, as found by DESeq2, showed much lower performances compared to those obtained with the set of features selected with the ML algorithm, thus confirming the validity of our methodology and the importance of using ML models with gene expression data from RNA sequencing of whole blood where the signal is significantly low. Furthermore, we notice how some of the genes selected by the ML algorithm are not DE between the class of PD and the class of HC subjects see  but noneage of onset classes (early-onset and late-onset PD subtypes) could reflect the heterogeneity of PD at different ages. In fact, it has been observed that PD patients with older age onset have more severe motor and non-motor burdens and a more widespread involvement of striatal structures [Last, the different average predicted probability between subjects that falls in different ructures .C, the number of features selected, and identified a set of around 500 genes listed in We used a robust ML approach to make predictions on PD from whole blood expression data. The studied cohort included 390 early stage drug-naive PD subjects and 189 age-matched HCs. After careful preprocessing, including batch correction and independent gene filtering, we used a feature selection procedure based on RF and re-sampling and an XGBoost algorithm to evaluate PD vs. HC classification performances within a nested 10-fold cross validation scheme. We explored classification performances for different values of This area of research is cutting edge and requires further investigation. A possible extension of our work could be the evaluation of the predictive power of the selected set of genes on an independent dataset. We are also working on a multi-modal approach that combines transcriptome data with epigenomic data (and other data possibly) with the final aim of increasing the predictive performances of our model.