A robust clustering strategy for stratification unveils unique patient subgroups in acutely decompensated cirrhosis.

BACKGROUND: Patient heterogeneity poses significant challenges for managing individuals and designing clinical trials, especially in complex diseases. Existing classifications rely on outcome-predicting scores, potentially overlooking crucial elements contributing to heterogeneity without necessarily impacting prognosis. METHODS: To address patient heterogeneity, we developed ClustALL, a computational pipeline that simultaneously faces diverse clinical data challenges like mixed types, missing values, and collinearity. ClustALL enables the unsupervised identification of patient stratifications while filtering for stratifications that are robust against minor variations in the population (population-based) and against limited adjustments in the algorithm's parameters (parameter-based). RESULTS: Applied to a European cohort of patients with acutely decompensated cirrhosis (n = 766), ClustALL identified five robust stratifications, using only data at hospital admission. All stratifications included markers of impaired liver function and number of organ dysfunction or failure, and most included precipitating events. When focusing on one of these stratifications, patients were categorized into three clusters characterized by typical clinical features; notably, the 3-cluster stratification showed a prognostic value. Re-assessment of patient stratification during follow-up delineated patients' outcomes, with further improvement of the prognostic value of the stratification. We validated these findings in an independent prospective multicentre cohort of patients from Latin America (n = 580). CONCLUSIONS: By applying ClustALL to patients with acutely decompensated cirrhosis, we identified three patient clusters. Following these clusters over time offers insights that could guide future clinical trial design. ClustALL is a novel and robust stratification method capable of addressing the multiple challenges of patient stratification in most complex diseases.

Generation of iPSC lines (KAIMRCi003A, KAIMRCi003B) from a Saudi patient with Dravet syndrome carrying homozygous mutation in the CPLX1 gene and heterozygous mutation in SCN9A.

The most prevalent form of epileptic encephalopathy is Dravet syndrome (DRVT), which is triggered by the pathogenic variant SCN1A in 80% of cases. iPSCs with different SCN1A mutations have been constructed by several groups to model DRVT syndrome. However, no studies involving DRVT-iPSCs with rare genetic variants have been conducted. Here, we established two DRVT-iPSC lines harboring a homozygous mutation in the CPLX1 gene and heterozygous mutation in SCN9A gene. Therefore, the derivation of these iPSC lines provides a unique cellular platform to dissect the molecular mechanisms underlying the cellular dysfunctions consequent to CPLX1 and SCN9A mutations.

HLA-based banking of induced pluripotent stem cells in Saudi Arabia.

BACKGROUND: Human iPSCs' derivation and use in clinical studies are transforming medicine. Yet, there is a high cost and long waiting time associated with autologous iPS-based cellular therapy, and the genetic engineering of hypo-immunogenic iPS cell lines is hampered with numerous hurdles. Therefore, it is increasingly interesting to create cell stocks based on HLA haplotype distribution in a given population. This study aimed to assess the potential of HLA-based iPS banking for the Saudi population. METHODS: In this study, we interrogated the HLA database of the Saudi Stem Cell Donor Registry (SSCDR), containing high-resolution HLA genotype data from 64,315 registered Saudi donors at the time of analysis. This database was considered to be a representative sample of the Saudi population. The most frequent HLA haplotypes in the Saudi population were determined, and an in-house developed iterative algorithm was used to identify their HLA matching percentages in the SSCDR database and cumulative coverage. Subsequently, to develop a clinically relevant protocol for iPSCs generation, and to illustrate the applicability of the concept of HLA-based banking for cell therapy purposes, the first HLA-based iPS cell line in Saudi Arabia was generated. Clinically relevant methods were employed to generate the two iPS clones from a homozygous donor for the most prevalent HLA haplotype in the Saudi population. The generated lines were then assessed for pluripotency markers, and their ability to differentiate into all three germ layers, beating cardiomyocytes, and neural progenitors was examined. Additionally, the genetic stability of the HLA-iPS cell lines was verified by comparing the mutational burden in the clones and the original blood sample, using whole-genome sequencing. The standards set by the American College of Medical Genetics and Genomics (ACMG) were used to determine the clinical significance of identified variants. RESULTS: The analysis revealed that the establishment of only 13 iPSC lines would match 30% of the Saudi population, 39 lines would attain 50% coverage, and 596 lines would be necessary for over 90% coverage. The proof-of-concept HLA-iPSCs, which cover 6.1% of the Saudi population, successfully demonstrated pluripotency and the ability to differentiate into various cell types including beating cardiomyocytes and neuronal progenitors. The comprehensive genetic analysis corroborated that all identified variants in the derived iPSCs were inherently present in the original donor sample and were classified as benign according to the standards set by the ACMG. CONCLUSIONS: Our study sets a road map for introducing iPS-based cell therapy in the Kingdom of Saudi Arabia. It underscores the pragmatic approach of HLA-based iPSC banking which circumvents the limitations of autologous iPS-based cellular therapies. The successful generation and validation of iPSC lines based on the most prevalent HLA haplotype in the Saudi population signify a promising step toward broadening the accessibility and applicability of stem cell therapies and regenerative medicine in Saudi Arabia.

DNA Methylation Signatures of Multiple Sclerosis Occur Independently of Known Genetic Risk and Are Primarily Attributed to B Cells and Monocytes.

Epigenetic mechanisms can regulate how DNA is expressed independently of sequence and are known to be associated with various diseases. Among those epigenetic mechanisms, DNA methylation (DNAm) is influenced by genotype and the environment, making it an important molecular interface for studying disease etiology and progression. In this study, we examined the whole blood DNA methylation profiles of a large group of people with (pw) multiple sclerosis (MS) compared to those of controls. We reveal that methylation differences in pwMS occur independently of known genetic risk loci and show that they more strongly differentiate disease (AUC = 0.85, 95% CI 0.82-0.89, p = 1.22 × 10-29) than known genetic risk loci (AUC = 0.72, 95% CI: 0.66-0.76, p = 9.07 × 10-17). We also show that methylation differences in MS occur predominantly in B cells and monocytes and indicate the involvement of cell-specific biological pathways. Overall, this study comprehensively characterizes the immune cell-specific epigenetic architecture of MS.

Generation of myoglobin (MB)-knockout human embryonic stem cell (hESC) line (KAIMRCe002-A-1S) using CRISPR/Cas9 technology.

Myoglobin (MB) is a cytoplasmic hemoprotein that is predominantly expressed in the heart and oxidative myofibers of skeletal muscle. It has been demonstrated that MB binds to oxygen and promotes its diffusion for energy production in the mitochondria. Recently, MB was found to be expressed in different forms of malignant tumors and cancer cell lines. Further studies using gene disruption technology will enhance the understanding of MB's role in human cardiovascular biology and cancers. Here, we describe the generation of a homozygous MB knockout in human embryonic stem cells (hESC-MB-/-) via CRISPR/Cas9 to study MB function in human biology and diseases.

scAEGAN: Unification of single-cell genomics data by adversarial learning of latent space correspondences.

Recent progress in Single-Cell Genomics has produced different library protocols and techniques for molecular profiling. We formulate a unifying, data-driven, integrative, and predictive methodology for different libraries, samples, and paired-unpaired data modalities. Our design of scAEGAN includes an autoencoder (AE) network integrated with adversarial learning by a cycleGAN (cGAN) network. The AE learns a low-dimensional embedding of each condition, whereas the cGAN learns a non-linear mapping between the AE representations. We evaluate scAEGAN using simulated data and real scRNA-seq datasets, different library preparations (Fluidigm C1, CelSeq, CelSeq2, SmartSeq), and several data modalities as paired scRNA-seq and scATAC-seq. The scAEGAN outperforms Seurat3 in library integration, is more robust against data sparsity, and beats Seurat 4 in integrating paired data from the same cell. Furthermore, in predicting one data modality from another, scAEGAN outperforms Babel. We conclude that scAEGAN surpasses current state-of-the-art methods and unifies integration and prediction challenges.

RNA-sequencing and mass-spectrometry proteomic time-series analysis of T-cell differentiation identified multiple splice variants models that predicted validated protein biomarkers in inflammatory diseases.

Profiling of mRNA expression is an important method to identify biomarkers but complicated by limited correlations between mRNA expression and protein abundance. We hypothesised that these correlations could be improved by mathematical models based on measuring splice variants and time delay in protein translation. We characterised time-series of primary human naïve CD4+ T cells during early T helper type 1 differentiation with RNA-sequencing and mass-spectrometry proteomics. We performed computational time-series analysis in this system and in two other key human and murine immune cell types. Linear mathematical mixed time delayed splice variant models were used to predict protein abundances, and the models were validated using out-of-sample predictions. Lastly, we re-analysed RNA-seq datasets to evaluate biomarker discovery in five T-cell associated diseases, further validating the findings for multiple sclerosis (MS) and asthma. The new models significantly out-performing models not including the usage of multiple splice variants and time delays, as shown in cross-validation tests. Our mathematical models provided more differentially expressed proteins between patients and controls in all five diseases. Moreover, analysis of these proteins in asthma and MS supported their relevance. One marker, sCD27, was validated in MS using two independent cohorts for evaluating response to treatment and disease prognosis. In summary, our splice variant and time delay models substantially improved the prediction of protein abundance from mRNA expression in three different immune cell types. The models provided valuable biomarker candidates, which were further validated in MS and asthma.

Deconvolution of the hematopoietic stem cell microenvironment reveals a high degree of specialization and conservation.

Understanding the regulation of normal and malignant human hematopoiesis requires comprehensive cell atlas of the hematopoietic stem cell (HSC) regulatory microenvironment. Here, we develop a tailored bioinformatic pipeline to integrate public and proprietary single-cell RNA sequencing (scRNA-seq) datasets. As a result, we robustly identify for the first time 14 intermediate cell states and 11 stages of differentiation in the endothelial and mesenchymal BM compartments, respectively. Our data provide the most comprehensive description to date of the murine HSC-regulatory microenvironment and suggest a higher level of specialization of the cellular circuits than previously anticipated. Furthermore, this deep characterization allows inferring conserved features in human, suggesting that the layers of microenvironmental regulation of hematopoiesis may also be shared between species. Our resource and methodology is a stepping-stone toward a comprehensive cell atlas of the BM microenvironment.

Data-driven bioinformatics to disentangle cells within a tissue microenvironment.

Molecular profiling of clinical tissue samples is at the core of precision medicine. Yet, to elucidate the contribution of mixed cell types and detect changes in cell populations in response to infections or drugs is challenging. Recent advances using machine learning promise to learn explanatory models directly from data.

Deep neural network prediction of genome-wide transcriptome signatures - beyond the Black-box.

Prediction algorithms for protein or gene structures, including transcription factor binding from sequence information, have been transformative in understanding gene regulation. Here we ask whether human transcriptomic profiles can be predicted solely from the expression of transcription factors (TFs). We find that the expression of 1600 TFs can explain >95% of the variance in 25,000 genes. Using the light-up technique to inspect the trained NN, we find an over-representation of known TF-gene regulations. Furthermore, the learned prediction network has a hierarchical organization. A smaller set of around 125 core TFs could explain close to 80% of the variance. Interestingly, reducing the number of TFs below 500 induces a rapid decline in prediction performance. Next, we evaluated the prediction model using transcriptional data from 22 human diseases. The TFs were sufficient to predict the dysregulation of the target genes (rho = 0.61, P < 10-216). By inspecting the model, key causative TFs could be extracted for subsequent validation using disease-associated genetic variants. We demonstrate a methodology for constructing an interpretable neural network predictor, where analyses of the predictors identified key TFs that were inducing transcriptional changes during disease.

Approximations of algorithmic and structural complexity validate cognitive-behavioral experimental results.

Being able to objectively characterize the intrinsic complexity of behavioral patterns resulting from human or animal decisions is fundamental for deconvolving cognition and designing autonomous artificial intelligence systems. Yet complexity is difficult in practice, particularly when strings are short. By numerically approximating algorithmic (Kolmogorov) complexity (K), we establish an objective tool to characterize behavioral complexity. Next, we approximate structural (Bennett's Logical Depth) complexity (LD) to assess the amount of computation required for generating a behavioral string. We apply our toolbox to three landmark studies of animal behavior of increasing sophistication and degree of environmental influence, including studies of foraging communication by ants, flight patterns of fruit flies, and tactical deception and competition (e.g., predator-prey) strategies. We find that ants harness the environmental condition in their internal decision process, modulating their behavioral complexity accordingly. Our analysis of flight (fruit flies) invalidated the common hypothesis that animals navigating in an environment devoid of stimuli adopt a random strategy. Fruit flies exposed to a featureless environment deviated the most from Levy flight, suggesting an algorithmic bias in their attempt to devise a useful (navigation) strategy. Similarly, a logical depth analysis of rats revealed that the structural complexity of the rat always ends up matching the structural complexity of the competitor, with the rats' behavior simulating algorithmic randomness. Finally, we discuss how experiments on how humans perceive randomness suggest the existence of an algorithmic bias in our reasoning and decision processes, in line with our analysis of the animal experiments. This contrasts with the view of the mind as performing faulty computations when presented with randomized items. In summary, our formal toolbox objectively characterizes external constraints on putative models of the "internal" decision process in humans and animals.

Deep characterization of paired chromatin and transcriptomes in four immune cell types from multiple sclerosis patients.

Background: The putative involvement of chromatin states in multiple sclerosis (MS) is thus far unclear. Here we determined the association of chromatin-accessibility with concurrent genetic, epigenetic and transcriptional events. Material & methods: We generated paired assay for transposase-accessible chromatin sequencing and RNA-sequencing profiles from sorted blood immune CD4+ and CD8+ T cells, CD14+ monocytes and CD19+ B cells from healthy controls (HCs) and MS patients. Results: We identified differentially accessible regions between MS patients and HCs, primarily in CD4+ and CD19+. CD4+ regions were enriched for MS-associated single nucleotide polymorphisms and differentially methylated loci. In the vicinity of differentially accessible regions of CD4+ cells, 42 differentially expressed genes were identified. The top two dysregulated genes identified in this multilayer analysis were CCDC114 and SERTAD1. Conclusion: These findings provide new insight into the primary role of CD4+ and CD19+ cells in MS.

Lay abstract Multiple sclerosis (MS) is a devastating disease that affects individuals at a young age and gradually worsens over their lifespan. Currently, treatment for MS is broad, meaning it treats the symptoms but not the cause of the disease. Treating symptoms means that patients may feel better, but their general quality of life is not normal. In addition, treating symptoms can lead to the underlying cause still being present, which can come back once treatment is stopped. What we are striving to do in this article is to better understand the cause. If we can do that, we can have targeted treatment that will get rid of the disease without the fear of it coming back and drastically improve quality of life and life span. Here, we have identified the complex nature of MS and made an effort to identify certain genes that are different in MS patients and present a way to better understand MS using advanced genome study methodologies.

DNA Transposon Expansion is Associated with Genome Size Increase in Mudminnows.

Genome sizes of eukaryotic organisms vary substantially, with whole-genome duplications (WGD) and transposable element expansion acting as main drivers for rapid genome size increase. The two North American mudminnows, Umbra limi and Umbra pygmaea, feature genomes about twice the size of their sister lineage Esocidae (e.g., pikes and pickerels). However, it is unknown whether all Umbra species share this genome expansion and which causal mechanisms drive this expansion. Using flow cytometry, we find that the genome of the European mudminnow is expanded similarly to both North American species, ranging between 4.5 and 5.4 pg per diploid nucleus. Observed blocks of interstitially located telomeric repeats in U. limi suggest frequent Robertsonian rearrangements in its history. Comparative analyses of transcriptome and genome assemblies show that the genome expansion in Umbra is driven by the expansion of DNA transposon and unclassified repeat sequences without WGD. Furthermore, we find a substantial ongoing expansion of repeat sequences in the Alaska blackfish Dallia pectoralis, the closest relative to the family Umbridae, which might mark the beginning of a similar genome expansion. Our study suggests that the genome expansion in mudminnows, driven mainly by transposon expansion, but not WGD, occurred before the separation into the American and European lineage.

Gene Regulatory Network of Human GM-CSF-Secreting T Helper Cells.

GM-CSF produced by autoreactive CD4-positive T helper cells is involved in the pathogenesis of autoimmune diseases, such as multiple sclerosis. However, the molecular regulators that establish and maintain the features of GM-CSF-positive CD4 T cells are unknown. In order to identify these regulators, we isolated human GM-CSF-producing CD4 T cells from human peripheral blood by using a cytokine capture assay. We compared these cells to the corresponding GM-CSF-negative fraction, and furthermore, we studied naïve CD4 T cells, memory CD4 T cells, and bulk CD4 T cells from the same individuals as additional control cell populations. As a result, we provide a rich resource of integrated chromatin accessibility (ATAC-seq) and transcriptome (RNA-seq) data from these primary human CD4 T cell subsets and we show that the identified signatures are associated with human autoimmune diseases, especially multiple sclerosis. By combining information about mRNA expression, DNA accessibility, and predicted transcription factor binding, we reconstructed directed gene regulatory networks connecting transcription factors to their targets, which comprise putative key regulators of human GM-CSF-positive CD4 T cells as well as memory CD4 T cells. Our results suggest potential therapeutic targets to be investigated in the future in human autoimmune disease.

Predicting anti-PD-1 responders in malignant melanoma from the frequency of S100A9+ monocytes in the blood.

BACKGROUND: While programmed cell death receptor 1 (PD-1) blockade treatment has revolutionized treatment of patients with melanoma, clinical outcomes are highly variable, and only a fraction of patients show durable responses. Therefore, there is a clear need for predictive biomarkers to select patients who will benefit from the treatment. METHOD: To identify potential predictive markers for response to PD-1 checkpoint blockade immunotherapy, we conducted single-cell RNA sequencing analyses of peripheral blood mononuclear cells (PBMC) (n=8), as well as an in-depth immune monitoring study (n=20) by flow cytometry in patients with advanced melanoma undergoing treatment with nivolumab at Karolinska University Hospital. Blood samples were collected before the start of treatment and at the time of the second dose. RESULTS: Unbiased single-cell RNA sequencing of PBMC in patients with melanoma uncovered that a higher frequency of monocytes and a lower ratio of CD4+ T cells to monocyte were inversely associated with overall survival. Similarly, S100A9 expression in the monocytic subset was correlated inversely with overall survival. These results were confirmed by a flow cytometry-based analysis in an independent patient cohort. CONCLUSION: Our results suggest that monocytic cell populations can critically determine the outcome of PD-1 blockade, particularly the subset expressing S100A9, which should be further explored as a possible predictive biomarker. Detailed knowledge of the biological role of S100A9+ monocytes is of high translational relevance.

STATegra: Multi-Omics Data Integration - A Conceptual Scheme With a Bioinformatics Pipeline.

Technologies for profiling samples using different omics platforms have been at the forefront since the human genome project. Large-scale multi-omics data hold the promise of deciphering different regulatory layers. Yet, while there is a myriad of bioinformatics tools, each multi-omics analysis appears to start from scratch with an arbitrary decision over which tools to use and how to combine them. Therefore, it is an unmet need to conceptualize how to integrate such data and implement and validate pipelines in different cases. We have designed a conceptual framework (STATegra), aiming it to be as generic as possible for multi-omics analysis, combining available multi-omic anlaysis tools (machine learning component analysis, non-parametric data combination, and a multi-omics exploratory analysis) in a step-wise manner. While in several studies, we have previously combined those integrative tools, here, we provide a systematic description of the STATegra framework and its validation using two The Cancer Genome Atlas (TCGA) case studies. For both, the Glioblastoma and the Skin Cutaneous Melanoma (SKCM) cases, we demonstrate an enhanced capacity of the framework (and beyond the individual tools) to identify features and pathways compared to single-omics analysis. Such an integrative multi-omics analysis framework for identifying features and components facilitates the discovery of new biology. Finally, we provide several options for applying the STATegra framework when parametric assumptions are fulfilled and for the case when not all the samples are profiled for all omics. The STATegra framework is built using several tools, which are being integrated step-by-step as OpenSource in the STATegRa Bioconductor package.

DeepViral: prediction of novel virus-host interactions from protein sequences and infectious disease phenotypes.

MOTIVATION: Infectious diseases caused by novel viruses have become a major public health concern. Rapid identification of virus-host interactions can reveal mechanistic insights into infectious diseases and shed light on potential treatments. Current computational prediction methods for novel viruses are based mainly on protein sequences. However, it is not clear to what extent other important features, such as the symptoms caused by the viruses, could contribute to a predictor. Disease phenotypes (i.e. signs and symptoms) are readily accessible from clinical diagnosis and we hypothesize that they may act as a potential proxy and an additional source of information for the underlying molecular interactions between the pathogens and hosts. RESULTS: We developed DeepViral, a deep learning based method that predicts protein-protein interactions (PPI) between humans and viruses. Motivated by the potential utility of infectious disease phenotypes, we first embedded human proteins and viruses in a shared space using their associated phenotypes and functions, supported by formalized background knowledge from biomedical ontologies. By jointly learning from protein sequences and phenotype features, DeepViral significantly improves over existing sequence-based methods for intra- and inter-species PPI prediction. AVAILABILITY AND IMPLEMENTATION: Code and datasets for reproduction and customization are available at https://github.com/bio-ontology-research-group/DeepViral. Prediction results for 14 virus families are available at https://doi.org/10.5281/zenodo.4429824. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

A robust machine learning framework to identify signatures for frailty: a nested case-control study in four aging European cohorts.

Phenotype-specific omic expression patterns in people with frailty could provide invaluable insight into the underlying multi-systemic pathological processes and targets for intervention. Classical approaches to frailty have not considered the potential for different frailty phenotypes. We characterized associations between frailty (with/without disability) and sets of omic factors (genomic, proteomic, and metabolomic) plus markers measured in routine geriatric care. This study was a prevalent case control using stored biospecimens (urine, whole blood, cells, plasma, and serum) from 1522 individuals (identified as robust (R), pre-frail (P), or frail (F)] from the Toledo Study of Healthy Aging (R=178/P=184/F=109), 3 City Bordeaux (111/269/100), Aging Multidisciplinary Investigation (157/79/54) and InCHIANTI (106/98/77) cohorts. The analysis included over 35,000 omic and routine laboratory variables from robust and frail or pre-frail (with/without disability) individuals using a machine learning framework. We identified three protective biomarkers, vitamin D3 (OR: 0.81 [95% CI: 0.68-0.98]), lutein zeaxanthin (OR: 0.82 [95% CI: 0.70-0.97]), and miRNA125b-5p (OR: 0.73, [95% CI: 0.56-0.97]) and one risk biomarker, cardiac troponin T (OR: 1.25 [95% CI: 1.23-1.27]). Excluding individuals with a disability, one protective biomarker was identified, miR125b-5p (OR: 0.85, [95% CI: 0.81-0.88]). Three risks of frailty biomarkers were detected: pro-BNP (OR: 1.47 [95% CI: 1.27-1.7]), cardiac troponin T (OR: 1.29 [95% CI: 1.21-1.38]), and sRAGE (OR: 1.26 [95% CI: 1.01-1.57]). Three key frailty biomarkers demonstrated a statistical association with frailty (oxidative stress, vitamin D, and cardiovascular system) with relationship patterns differing depending on the presence or absence of a disability.

MYC as a driver of stochastic chromatin networks: implications for the fitness of cancer cells.

The relationship between stochastic transcriptional bursts and dynamic 3D chromatin states is not well understood. Using an innovated, ultra-sensitive technique, we address here enigmatic features underlying the communications between MYC and its enhancers in relation to the transcriptional process. MYC thus interacts with its flanking enhancers in a mutually exclusive manner documenting that enhancer hubs impinging on MYC detected in large cell populations likely do not exist in single cells. Dynamic encounters with pathologically activated enhancers responsive to a range of environmental cues, involved <10% of active MYC alleles at any given time in colon cancer cells. Being the most central node of the chromatin network, MYC itself likely drives its communications with flanking enhancers, rather than vice versa. We submit that these features underlie an acquired ability of MYC to become dynamically activated in response to a diverse range of environmental cues encountered by the cell during the neoplastic process.

Harmonization of quality metrics and power calculation in multi-omic studies.

Multi-omic studies combine measurements at different molecular levels to build comprehensive models of cellular systems. The success of a multi-omic data analysis strategy depends largely on the adoption of adequate experimental designs, and on the quality of the measurements provided by the different omic platforms. However, the field lacks a comparative description of performance parameters across omic technologies and a formulation for experimental design in multi-omic data scenarios. Here, we propose a set of harmonized Figures of Merit (FoM) as quality descriptors applicable to different omic data types. Employing this information, we formulate the MultiPower method to estimate and assess the optimal sample size in a multi-omics experiment. MultiPower supports different experimental settings, data types and sample sizes, and includes graphical for experimental design decision-making. MultiPower is complemented with MultiML, an algorithm to estimate sample size for machine learning classification problems based on multi-omic data.