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The rapid growth in biomedical research has generated vast amounts of data, including genomic, molecular, imaging, and clinical information from humans and other species. Leveraging this data is essential for groundbreaking scientific discoveries and a deeper understanding of health and disease across different species. However, the complexity and volume of these datasets present significant computational challenges, limiting their potential.This dissertation addresses two key challenges in biomedical data analysis: the efficient evaluation of sequencing data and the effective management and analysis of gene sets. By focusing on these areas, we develop innovative computational methods that enable the rapid, scalable, and accurate processing of large-scale biomedical data. For sequencing data, we create algorithms that enhance the speed and precision of data evaluation, making it feasible to manage the increasing volume of sequences generated by modern technologies. For gene sets, we devise tools for their efficient management and analysis, allowing researchers to draw meaningful insights from complex genetic information.Through this research, we aim to contribute to the development of new analytical tools and methods, ultimately supporting the advancement of precision medicine and personalized healthcare for both human and veterinary applications.

Proteins are essential to life. Precise understanding of protein functions is critical in addressing many biomedical questions. Different protein isoforms can be produced from a single gene through alternative splicing, which greatly expands the diversity of proteins and the complexity of cellular functions. However, precise annotations that differentiate functions of isoforms are few. On the other hand, effective modeling of functional knowledge can empower computational methods in many biological applications. A fundamental step in such applications is the discovery of gene sets. Methods that can accurately map genotypes to phenotypes are needed for detecting novel connections between gene sets derived from different experiments, which could enable new biological discoveries.Along with the accumulation of large-scale biological data, deep learning applications to biological data analysis are flourishing. In this dissertation, we propose three deep learning methods for the two related problems in functional genomics, i.e., producing high-resolution functional annotation at the isoform level, and discovering connections between experimentally derived gene sets via functional knowledge. First, we design DIFFUSE, which for the first time integrates isoform sequences and expression profiles to systematically predict isoform functions, by combining the power of deep learning and probabilistic graphical models. Second, to enhance the prediction of isoform functions, we propose FINER, which jointly predicts isoform functions and isoform-isoform interactions through the introduction of a unified learning objective, enabling the two tasks to benefit from each other. Finally, we develop FEGS, a representation learning approach based on hypergraph embedding, which embeds gene sets as compact features encoding functional information of gene members and facilitates gene set comparison by more sensitive detection of common phenotypes. FINER and DIFFUSE significantly outperform the existing isoform function prediction methods, and their predictions are validated by independent biological data. FEGS has been successfully applied to drug discovery and cell type identification.

Molecular biomarkers are certain molecules or set of molecules that can be of help for diagnosis or prognosis of diseases or disorders. In the past decades, thanks to the advances in high-throughput technologies, a huge amount of molecular 'omics' data, e.g., transcriptomics and proteomics, have been accumulated. The availability of these omics data makes it possible to screen biomarkers for diseases or disorders. Accordingly, a number of computational approaches have been developed to identify biomarkers by exploring the omics data. In this review, we present a comprehensive survey on the recent progress of identification of molecular biomarkers with machine learning approaches. Specifically, we categorize the machine learning approaches into supervised, un-supervised and recommendation approaches, where the biomarkers including single genes, gene sets and small gene networks. In addition, we further discuss potential problems underlying bio-medical data that may pose challenges for machine learning, and provide possible directions for future biomarker identification.

Purpose It is critical to identify putative causal targets for SARS coronavirus 2, which may guide drug repurposing options to reduce the public health burden of COVID-19. Methods We applied complementary methods and multiphased design to pinpoint the most likely causal genes for COVID-19 severity. First, we applied cross-methylome omnibus (CMO) test and leveraged data from the COVID-19 Host Genetics Initiative (HGI) comparing 9,986 hospitalized COVID-19 patients and 1,877,672 population controls. Second, we evaluated associations using the complementary S-PrediXcan method and leveraging blood and lung tissue gene expression prediction models. Third, we assessed associations of the identified genes with another COVID-19 phenotype, comparing very severe respiratory confirmed COVID versus population controls. Finally, we applied a fine-mapping method, fine-mapping of gene sets (FOGS), to prioritize putative causal genes. Results Through analyses of the COVID-19 HGI using complementary CMO and S-PrediXcan methods along with fine-mapping, XCR1, CCR2, SACM1L, OAS3, NSF, WNT3, NAPSA, and IFNAR2 are identified as putative causal genes for COVID-19 severity. Conclusion We identified eight genes at five genomic loci as putative causal genes for COVID-19 severity.

The human visual cortex is a heterogeneous entity that has multiple subregions showing substantial variability in their functions and connections. We aimed to identify genes associated with resting-state functional connectivity (rsFC) of visual subregions using transcriptome-neuroimaging spatial correlations in discovery and validation datasets. Results showed that rsFC of eight visual subregions were associated with expression measures of eight gene sets, which were specifically expressed in brain tissue and showed the strongest correlations with visual behavioral processes. Moreover, there was a significant divergence in these gene sets and their functional features between medial and lateral visual subregions. Relative to those associated with lateral subregions, more genes associated with medial subregions were found to be enriched for neuropsychiatric diseases and more diverse biological functions and pathways, and to be specifically expressed in multiple types of neurons and immune cells and during the middle and late stages of cortical development. In addition to shared behavioral processes, lateral subregion associated genes were uniquely correlated with high-order cognition. These findings of commonalities and differences in the identified rsFC-related genes and their functional features across visual subregions may improve our understanding of the functional heterogeneity of the visual cortex from the perspective of underlying genetic architecture.

The minimal gene set for life has often been theorised, with at least ten produced forMycoplasma genitalium(M. genitalium). Due to the difficulty of usingM. genitaliumin the lab, combined with its long replication time of 12 - 15 hours, none of these theoretical minimal genomes have been tested, even with modern techniques. The publication of theM. genitaliumwhole-cell model provided the first opportunity to test them, simulating the genome editsin-silico. We simulated eight minimal gene sets from the literature, finding that they producedin-silicocells that did not divide. Using knowledge from previous research, we reintroduced specific essential and low essential genesin-silico; enabling cellular division. This reinforces the need to identify species-specific low essential genes and their interactions. Any genome designs created using the currently incomplete and fragmented gene essentiality information, will very likely requirein-vivoreintroductions to correct issues and produce dividing cells.

We previously characterized the molecular changes in acute kidney injury (AKI) and chronic kidney disease (CKD) in kidney transplant biopsies, but parenchymal changes selective for specific types of injury could be missed by such analyses. The present study searched for injury changes beyond AKI and CKD related to specific scenarios, including correlations with donor age. We defined injury using previously defined gene sets and classifiers and used principal component analysis to discover new injury dimensions. As expected, Dimension 1 distinguished normal vs. injury, and Dimension 2 separated early AKI from late CKD, correlating with time posttransplant. However, Dimension 3 was novel, distinguishing a set of genes related to epithelial polarity (e.g., PARD3) that were increased in early AKI and decreased in T cell-mediated rejection (TCMR) but not in antibody-mediated rejection. Dimension 3 was increased in kidneys from older donors and was particularly important in survival of early kidneys. Thus high Dimension 3 scores emerge as a previously unknown element in the kidney response-to-injury that affects epithelial polarity genes and is increased in AKI but depressed in TCMR, indicating that in addition to general injury elements, certain injury elements are selective for specific pathologic mechanisms. (ClinicalTrials.gov NCT01299168).

Background Colorectal cancer (CRC) is the third most common cancer worldwide. The opening of the TCGA and GEO databases has promoted the progress of CRC prognostic assessment, while the aging-related risk signature has never been mentioned. Methods R software packages, GSEA software, Venn diagram, Metascape, STRING, Cytoscape, cBioPortal, TIMER and GeneMANIA website were used in this study. Results Aging-related gene sets, GO_AGING, GO_CELL_AGING and GO_CELLULAR_SENESCENCE, were activated significantly in CRC tissues. We constructed an aging-related risk signature using LASSO COX regression in training group TCGA and validated in testing group GSE39582. The risk score was significantly associated with the overall survival of CRC patients, whose stability was clarified by stratified survival analysis and accuracy was demonstrated using the ROC curve. The risk score was significantly increased in the advanced stage, T3-4, N1-3 and M1 and positively correlated with the richness of immune cell infiltration in the tumor microenvironment. We further investigated the molecular characteristics of 15 hub genes at the DNA and protein levels and performed GSEA between high- and low-risk groups. Conclusions The aging-related signature is a reliable prognostic analysis model and can predict the severity and immune cell infiltration of CRC patients.

Single-cell RNA-Sequencing (scRNA-Seq) has improved our understanding of individual cell types in the human placenta. However, placental scRNA-Seq data is not readily accessible when trying to understand how expression patterns in model systems correspond to those from first trimester human placenta. Therefore, we developed PlacentaCellEnrich, a tool that takes a gene set as input, and then reports if the input set is enriched for genes with placenta cell-specific expression patterns, based on human placenta scRNA-Seq data. The PlacentaCellEnrich tool is freely available at https://placentacellenrich.gdcb.iastate.edu/ for non-profit academic use under the MIT license.

The L1000 dataset, containing gene microarray data from 978 landmark genes has been previously shown to accurately predict expression of ~81% of the remaining 21,290 target genes. Microarray data was utilized to characterize groups of tissue types within the L1000 dataset to assess whether 978 landmark genes, compared to non-landmark genes, would better differentiate samples into clusters containing distinct tissue types. Landmark genes better differentiated k-means clusters, compared to non-landmark genes. These results suggest that landmark genes better characterize heterogeneous samples in their comprehensive genetic profile. Our previous studies showed that categorical separation of samples based on clinical or biological groups generally improves when studying heterogeneous sample types when using landmark genes as features, compared to non-landmark genes. However, the present work indicates that non-landmark genes may also be utilized to separate samples in clustering when there is a large sample size present for training k-means clustering models. In contrast, when studying a small sample size of the same set of heterogenous samples, landmark genes as features improve clustering. This study has implications for assessing various tissue types as landmark genes may be directly measured to predict categorical sample qualities as well as expression of remaining target genes.

Mutual exclusivity analyses provide an effective tool to identify driver genes from passenger genes for cancer studies. Various algorithms have been developed for the detection of mutual exclusivity, but controlling false positive and improving accuracy remain challenging. We propose a forward selection algorithm for identification of mutually exclusive gene sets (FSME) in this paper. The method includes an initial search of seed pair of mutually exclusive (ME) genes and subsequently including more genes into the current ME set. Simulations demonstrated that, compared to recently published approaches (i.e., CoMEt, WExT, and MEGSA), FSME could provide higher precision or recall rate to identify ME gene sets, and had superior control of false positive rates. With application to TCGA real data sets for AML, BRCA, and GBM, we confirmed that FSME can be utilized to discover cancer driver genes.

Essential genes refer to genes that are required by an organism to survive under specific conditions. Studies of the minimal-gene-set for bacteria have elucidated fundamental cellular processes that sustain life. The past five years have seen a significant progress in identifying human essential genes, primarily due to the successful use of CRISPR/Cas9 in various types of human cells. DEG 15, a new release of the Database of Essential Genes (www.essentialgene.org), has provided major advancements, compared to DEG 10. Specifically, the number of eukaryotic essential genes has increased by more than fourfold, and that of prokaryotic ones has more than doubled. Of note, the human essential-gene number has increased by more than tenfold. Moreover, we have developed built-in analysis modules by which users can perform various analyses, such as essential-gene distributions between bacterial leading and lagging strands, sub-cellular localization distribution, enrichment analysis of gene ontology and KEGG pathways, and generation of Venn diagrams to compare and contrast gene sets between experiments. Additionally, the database offers customizable BLAST tools for performing species- and experiment-specific BLAST searches. Therefore, DEG comprehensively harbors updated human-curated essential-gene records among prokaryotes and eukaryotes with built-in tools to enhance essential-gene analysis.

The present study examined the demographic correlates of gene expression in a sample of adults (n = 543) from the Study of Midlife in the United States (MIDUS). Inflammatory and antiviral gene sets were operationalized using a priori composite scores and empirically derived co-regulatory gene sets. For both composite scores and co-regulatory gene sets, White/European Americans showed lower while Black/African Americans showed higher expression of genes involved in interferon responses and antibody synthesis. The effects of chronological age on gene expression varied by sex, such that pro-inflammatory gene expression increased with age more rapidly for females than males. The difference between the average expression of inflammatory and antiviral genes also increased with age for females but not males. Results shed light on differential gene expression as a potential physiological correlate for race/ethnicity, age, and sex-related health disparities in adulthood.

BACKGROUND. Outcome of breast cancer (BC) in African American females (AA) remains worse than Caucasian females (CA) after accounting for socioeconomic factors and tumor characteristics. Heterogeneity of tumor immune microenvironment (TIME) composition demonstrated varying roles of infiltrating lymphocytes (TILs) in tumor progression and clinical outcome. We hypothesize that different racial TILs composition impacts downstream tumor signaling and worsens AA BC outcome. METHODS. The Cancer Genome Atlas (TCGA) harmonized RNA-Seq HTSeq and 450K Methylation data were accessed from GDC. Clinical and CIBERSORT immune composition data downloaded from final PANCAN publications. Outcome data included overall survival (OS), disease specific survival (DSS) and disease free interval (DFI). Differential expression and gene set enrichment analysis (GSEA) were performed using R packages limma and ClusterProfiler. FDR corrected p values < 0.05 were considered significant. RESULTS. Of 182 AA and 755 CA, TNBC and Basal subtypes were higher in AA (33% and 34% vs 14% and 13% respectively). OS, DSS, DFI and PFI were worse in AA but did not reach significance. Within TNBC, OS of AA was worse than CA (HR 2.18[1.04-4.56]). Compared to CA, AA had higher TILs, T-Regulatory (Tregs) and T-Follicular Helper cells but lower M2 Macrophages (all p CONCLUSION. AA had overall more immune infiltrates and different composition of BC TIME compared to CA, with higher immunosuppressive fractions. Tregs were persistently higher across all IHC subtypes, predicted worse outcome and generated higher T-cell exhaustion signature, as well as downstream Treg-specific enriched chemotaxis and activation gene sets in AA compared to CA. These data support the hypothesis of adverse TIME in AA and opens venues of personalized TIME modulation. Citation Format: Ahmed Elkhanany, Eriko Katsuta, Kazuaki Takabe. Detrimental impact of T-regulatory cells on outcome of breast cancer in African American population [abstract]. In: Proceedings of the 2019 San Antonio Breast Cancer Symposium; 2019 Dec 10-14; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2020;80(4 Suppl):Abstract nr P2-10-03.

Although the term "asthma" has been applied to all patients with airway lability and variable chest symptoms for centuries, phenotypes of asthma with distinct clinical and molecular features that may warrant different treatment approaches are well recognized. Patients with type 2 (T2)-"high" asthma are characterized by upregulation of T2 immune pathways (ie, IL-4 and IL-13 gene sets) and eosinophilic airway inflammation, whereas these features are absent in patients with T2-"low" asthma and may contribute to poor responsiveness to corticosteroid treatment. This review details definitions and clinical features of T2-"low" asthma, potential mechanisms and metabolic aspects, pediatric considerations, and potential treatment approaches. Priority research questions for T2-"low" asthma are also discussed.

Molecular prognosis markers hold promise for improved prediction of patient survival, and a pathway or gene set may add mechanistic interpretation to their prognostic prediction power. In this study, we demonstrated a novel strategy to identify prognosis-relevant gene sets in cancers. Our study consists of a first round of gene-level analyses and a second round of gene-set-level analyses, in which the Composite Gene Expression Score critically summarizes a surrogate expression value at gene set level and a permutation procedure is exerted to assess prognostic significance of gene sets. An optional differential coexpression module is appended to the two phases of survival analyses to corroborate and refine prognostic gene sets. Our strategy was demonstrated in 33 cancer types across 32,234 gene sets. We found oncogenic gene sets accounted for an increased proportion among the final gene sets, and genes involved in DNA replication and DNA repair have ubiquitous prognositic value for multiple cancer types. In summary, we carried out the largest gene set based prognosis study to date. Compared to previous similar studies, our approach offered multiple improvements in design and methodology implementation. Functionally relevant gene sets of ubiquitous prognostic significance in multiple cancer types were identified.

Background Microalgae are emerging as promising sustainable sources for biofuels, biostimulants in agriculture, soil bioremediation, feed and human nutrients. Nonetheless, the molecular mechanisms underpinning microalgae physiology and the biosynthesis of compounds of biotechnological interest are largely uncharacterized. This hinders the development of microalgae full potential as cell-factories. The recent application of omics technologies into microalgae research aims at unraveling these systems. Nevertheless, the lack of specific tools for analysing omics raw data generated from microalgae to provide biological meaningful information are hampering the impact of these technologies. The purpose of ALGAEFUN with MARACAS consists in providing researchers in microalgae with an enabling tool that will allow them to exploit transcriptomic and cistromic high-throughput sequencing data. Results ALGAEFUN with MARACAS consists of two different tools. First, MARACAS (MicroAlgae RnA-seq and Chip-seq AnalysiS) implements a fully automatic computational pipeline receiving as input RNA-seq (RNA sequencing) or ChIP-seq (chromatin immunoprecipitation sequencing) raw data from microalgae studies. MARACAS generates sets of differentially expressed genes or lists of genomic loci for RNA-seq and ChIP-seq analysis respectively. Second, ALGAEFUN (microALGAE FUNctional enrichment tool) is a web-based application where gene sets generated from RNA-seq analysis as well as lists of genomic loci from ChIP-seq analysis can be used as input. On the one hand, it can be used to perform Gene Ontology and biological pathways enrichment analysis over gene sets. On the other hand, using the results of ChIP-seq data analysis, it identifies a set of potential target genes and analyses the distribution of the loci over gene features. Graphical representation of the results as well as tables with gene annotations are generated and can be downloaded for further analysis. Conclusions ALGAEFUN with MARACAS provides an integrated environment for the microalgae research community that facilitates the process of obtaining relevant biological information from raw RNA-seq and ChIP-seq data. These applications are designed to assist researchers in the interpretation of gene lists and genomic loci based on functional enrichment analysis. ALGAEFUN with MARACAS is publicly available on https://greennetwork.us.es/AlgaeFUN/.

Purpose Claudin-low molecular subtypes have been identified in breast and bladder cancers and are characterized by low expression of claudins, enrichment for epithelial-to-mesenchymal transition (EMT), and tumor-initiating cell (TIC) features. We evaluated whether the claudin-low subtype also exists in gastric cancer. Materials and Methods Four hundred fifteen tumors from The Cancer Genome Atlas (TCGA) gastric cancer mRNA data set were clustered on the claudin, EMT, and TIC gene sets to identify claudin-low tumors. We derived a 24-gene predictor that classifies gastric cancer into claudin-low and non–claudin-low subtypes. This predictor was validated with the Asian Cancer Research Group (ACRG) data set. We characterized molecular and clinical features of claudin-low tumors. Results We identified 46 tumors that had consensus enrichment for claudin-low features in TCGA data set. Claudin-low tumors were most commonly diffuse histologic type (82%) and originally classified as TCGA genomically stable (GS) subtype (78%). Compared with GS subtype, claudin-low subtype had significant activation in Rho family of GTPases signaling, which appears to play a key role in its EMT and TIC properties. In the ACRG data set, 28 of 300 samples were classified as claudin-low tumors by the 24-gene predictor and were phenotypically similar to the initially derived claudin-low tumors. Clinically, claudin-low subtype had the worst overall survival. Of note, the hazard ratios that compared claudin-low versus GS subtype were 2.10 (95% CI, 1.07 to 4.11) in TCGA and 2.32 (95% CI, 1.18 to 4.55) in the ACRG cohorts, with adjustment for age and pathologic stage. Conclusion We identified a gastric claudin-low subtype that carries a poor prognosis likely related to therapeutic resistance as a result of its EMT and TIC phenotypes.

Survival analysis is a technique to identify prognostic biomarkers and genetic vulnerabilities in cancer studies. Large-scale consortium-based projects have profiled >11,000 adult and >4,000 paediatric tumor cases with clinical outcomes and multi-omics approaches. This provides a resource for investigating molecular-level cancer etiologies using clinical correlations. Although cancers often arise from multiple genetic vulnerabilities and have deregulated gene sets (GSs), existing survival analysis protocols can report only on individual genes. Additionally, there is no systematic method to connect clinical outcomes with experimental (cell line) data. To address these gaps, we developed cSurvival (https://tau.cmmt.ubc.ca/cSurvival). cSurvival provides a user-adjustable analytical pipeline with a curated, integrated database, and offers three main advances: (a) joint analysis with two genomic predictors to identify interacting biomarkers, including new algorithms to identify optimal cutoffs for two continuous predictors; (b) survival analysis not only at the gene, but also the GS level; and (c) integration of clinical and experimental cell line studies to generate synergistic biological insights. To demonstrate these advances, we report three case studies. We confirmed findings of autophagy-dependent survival in colorectal cancers and of synergistic negative effects between high expression of SLC7A11 and SLC2A1 on outcomes in several cancers. We further used cSurvival to identify high expression of the Nrf2-antioxidant response element pathway as a main indicator for lung cancer prognosis and for cellular resistance to oxidative stress-inducing drugs. Together, these analyses demonstrate cSurvival’s ability to support biomarker prognosis and interaction analysis via gene- and GS-level approaches and to integrate clinical and experimental biomedical studies.Key pointsWe developed cSurvival, an advanced framework using clinical correlations to study biomarker interactions in cancers, with source code and curated datasets freely available for allcSurvival includes new algorithms to identify optimal cutoffs for two continuous predictors to stratify patients into risk groups, enabling for the first time joint analysis with two genomic predictors;cSurvival allows survival analysis at the gene set (GS) level with comprehensive and up-to-date GS librariesThe cSurvival pipeline integrates clinical outcomes and experimental cancer cell line data to generate synergistic biological insights and to mine for appropriate preclinical cell line toolscSurvival is built on a manually curated cancer outcomes database

The identification of condition-specific gene sets from transcriptomic experiments is important to reveal regulatory and signaling mechanisms associated with a given cellular response. Statistical approaches using only expression data allow the identification of genes whose expression is most altered between different conditions. However, a phenotype is rarely a direct consequence of the activity of a single gene, but rather reflects the interplay of several genes to carry out certain molecular processes. Many methods have been proposed to analyze the activity of genes in light of our knowledge of their molecular interactions. However, existing methods have many limitations that make them of limited use to biologists: they detect modules that are too large, too small, or they require the users to specify a priori the size of the modules they are looking for.We propose AMINE (Active Module Identification through Network Embedding), an efficient method for the identification of active modules. Experiments carried out on artificial data sets show that the results obtained are more reliable than many available methods. Moreover, the size of the modules to be identified is not a fixed parameter of the method and does not need to be specified; rather, it adjusts according to the size of the modules to be found. The applications carried out on real datasets show that the method enables to find important genes already highlighted by approaches solely based on gene variations, but also to identify new groups of genes of high interest. In addition, AMINE method can be used as a web service on your own data (http://amine.i3s.unice.fr).

Background Patient-derived bulk expression profiles of cancers can provide insight into the transcriptional changes that underlie reprogrammed metabolism in cancer. These profiles represent the average expression pattern of all heterogeneous tumor and non-tumor cells present in biopsies of tumor lesions. Hence, subtle transcriptional footprints of metabolic processes can be concealed by other biological processes and experimental artifacts. However, consensus independent component analyses (c-ICA) can capture statistically independent transcriptional footprints of both subtle and more pronounced metabolic processes. Methods We performed c-ICA with 34,494 bulk expression profiles of patient-derived tumor biopsies, non-cancer tissues, and cell lines. Gene set enrichment analysis with 608 gene sets that describe metabolic processes was performed to identify the transcriptional components enriched for metabolic processes (mTCs). The activity of these mTCs was determined in all samples to create a metabolic transcriptional landscape. Results A set of 555 mTCs was identified of which many were robust across different datasets, platforms, and patient-derived tissues and cell lines. We demonstrate how the metabolic transcriptional landscape defined by the activity of these mTCs in samples can be used to explore the associations between the metabolic transcriptome and drug sensitivities, patient outcomes, and the composition of the immune tumor microenvironment. Conclusions To facilitate the use of our transcriptional metabolic landscape, we have provided access to all data via a web portal (www.themetaboliclandscapeofcancer.com). We believe this resource will contribute to the formulation of new hypotheses on how to metabolically engage the tumor or its (immune) microenvironment.

Gene set analysis has been widely used to gain insight from high-throughput expression studies. Although various tools and methods have been developed for gene set analysis, there is no consensus among researchers regarding best practice(s). Most often, evaluation studies have reported contradictory recommendations of which methods are superior. Therefore, an unbiased quantitative framework for evaluations of gene set analysis methods will be valuable. Such a framework requires gene expression datasets where enrichment status of gene sets is known a priori. In the absence of such gold standard datasets, artificial datasets are commonly used for evaluations of gene set analysis methods, however, they often rely on oversimplifying assumptions that make them biased in favor of or against a given method. In this paper, we propose a quantitative framework for evaluation of gene set analysis methods by synthesizing expression datasets using real data, without relying on oversimplifying or unrealistic assumptions, while preserving complex gene–gene correlations and retaining the distribution of expression values. The utility of the quantitative approach is shown by evaluating ten widely used gene set analysis methods. An implementation of the proposed method is publicly available. We suggest using Silver to evaluate existing and new gene set analysis methods. Evaluation using Silver provides a better understanding of current methods and can aid in the development of gene set analysis methods to achieve higher specificity without sacrificing sensitivity.

Although the health benefits of exercise in adults with obesity are well described, the direct effects of exercise on adipose tissue that may lead to improved metabolic health are poorly understood. The primary aims of this study were to perform an unbiased analysis of the subcutaneous abdominal adipose tissue transcriptomic response to acute exercise in adults with obesity, and to compare the effects of moderate-intensity continuous exercise versus high-intensity interval exercise on this response. Twenty-nine adults with obesity performed a session of either high-intensity interval exercise (HI; 10 × 1 min at 90%HRpeak, 1 min recovery between intervals; n = 14) or moderate-intensity continuous exercise (MI; 45 min at 70%HRpeak; n = 15). Groups were well matched for BMI (HI 33 ± 3 vs. MI 33 ± 4 kg/m(2)), sex (HI: 9 women vs. MI: 10 women), and age (HI: 32 ± 6 vs. MI: 29 ± 5). Subcutaneous adipose tissue was collected before and 1 h after the session of HI or MI, and samples were processed for RNA sequencing. Gene set enrichment analysis revealed 7 of 21 gene sets enriched postexercise overlapped between HI and MI. Interestingly, both HI and MI upregulated gene sets involved in inflammation (IL6-JAK-STAT3 signaling, allograft rejection, TNFα signaling via NFκB, and inflammatory response; FDR q value < 0.25). Exercise also downregulated adipogenic and oxidative metabolism gene sets in both groups. Overall, these data suggest genes involved in subcutaneous adipose tissue metabolism and inflammation may be an important part of the initial response after a session of exercise. NEW & NOTEWORTHY This study compared the effects of a single session of high-intensity interval exercise versus moderate-intensity continuous exercise on transcriptional changes in subcutaneous abdominal adipose tissue collected from adults with obesity. Our novel findings indicate exercise upregulated inflammation-related gene sets, while it downregulated metabolism-related gene sets – after both high-intensity and moderate-intensity exercise. These data suggest exercise can alter the adipose tissue transcriptome 1 h after exercise in ways that may impact inflammation and metabolism.

Cancers, a group of multifactorial complex diseases, are generally caused by mutation of multiple genes or dysregulation of gene interactions. Applying machine learning methods to microarray gene expression profiles for disease classification is a popular method to predict disease state or outcome. Traditional computational methods that detect genes differentially expressed between cancer and normal samples are ineffective in independent cohorts of patients. However, current methods consider pathways as simple gene sets and include pathway topological information but ignore significant individual genes and interactions between genes, which are essential to infer a more robust pathway activity. In this study, we proposed a novel approach to describe the activity of a pathway that incorporates both the differential expression degree of genes between the case and control and the interaction strength between genes. We applied the method to the classifications of seven cancers. Within-dataset experiments and cross-dataset experiments demonstrated that our novel method achieved robust and superior performance when compared to the five existing methods.