相关论文
2009
Bayesian Hierarchical Latent Model for Gene Set Analysis

Pathway is a set of genes which are predefined and serve a particular celluar or physiological function. Ranking pathways relevant to a particular phenotype can help researchers focus on a few sets of genes in pathways. In this thesis, a Bayesian hierarchical latent model was proposed using generalized linear random effects model. The advantage of the approach was that it can easily incorporate prior knowledge when the sample size was small and the number of genes was large. For the covariance matrix of a set of random variables, two Gaussian random processes were considered to construct the dependencies among genes in a pathway. One was based on the polynomial kernel and the other was based on the Gaussian kernel. Then these two kernels were compared with constant covariance matrix of the random effect by using the ratio, which was based on the joint posterior distribution with respect to each model. For mixture models, log-likelihood values were computed at different values of the mixture proportion, compared among mixtures of selected kernels and point-mass density (or constant covariance matrix). The approach was applied to a data set (Mootha et al., 2003) containing the expression profiles of type II diabetes where the motivation was to identify pathways that can discriminate between normal patients and patients with type II diabetes.

2020
Pan-Cancer Analysis of Pathway-Based Gene Expression Pattern at the Individual Level Reveals Novel Biomarkers of Clinical Prognosis

Identifying biomarkers to predict the clinical outcomes of individual patients is a fundamental problem in clinical oncology. Multiple single-gene biomarkers have already been identified and used in the clinics. However, multiple oncogenes or tumor-suppressor genes are involved during the process of tumorigenesis for each individual patient. Additionally, the efficacy of single-gene biomarkers is limited by the extensively variable expression levels measured by high-throughput assays. Inspired by the success of Gene Set Enrichment Analysis (GSEA), in this study, we advocate for the use of individual-level expression patterns of pre-defined pathways or gene sets as biomarkers. We hypothesize that in individual tumor samples, the disruption of transcription homeostasis plays an important role in tumorigenesis and has profound implications for the patient’s clinical outcome. To test this hypothesis, we conducted a pan-cancer analysis by studying tumor samples across 14 cancer types from The Cancer Genome Atlas. We devised a novel computational method named iPath to identify, at the individual sample level, which pathways or gene sets significantly deviate from their norms. We demonstrated that iPath is capable of identifying highly predictive biomarkers for clinical outcomes, including overall survival, tumor subtypes, and tumor stage classifications. Compared to single-gene biomarkers, we found that pathway-based biomarkers are more robust and effective. We are able to validate top biomarker pathways using random permutations as well as online SurvExpress resource with independent datasets.

2019 - Advanced Drug Delivery Reviews
Advances on non‐invasive physically triggered nucleic acid delivery from nanocarriers

&NA; Nucleic acids (NAs) have been considered as promising therapeutic agents for various types of diseases. However, their clinical applications still face many limitations due to their charge, high molecular weight, instability in biological environment and low levels of transfection. To overcome these drawbacks, therapeutic NAs should be carried in a stable nanocarrier, which can be viral or non‐viral vectors, and released at specific target site. Various controllable gene release strategies are currently being evaluated with interesting results. Endogenous stimuli‐responsive systems, for example pH‐, redox reaction‐, enzymatic‐triggered approaches have been widely studied based on the physiological differences between pathological and normal tissues. Meanwhile, exogenous triggered release strategies require the use of externally non‐invasive physical triggering signals such as light, heat, magnetic field and ultrasound. Compared to internal triggered strategies, external triggered gene release is time and site specifically controllable through active management of outside stimuli. The signal induces changes in the stability of the delivery system or some specific reactions which lead to endosomal escape and/or gene release. In the present review, the mechanisms and examples of exogenous triggered gene release approaches are detailed. Challenges and perspectives of such gene delivery systems are also discussed.

2018 - 2018 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)
Sample Size and Reproducibility of Gene Set Analysis

Gene set analysis is widely used to gain insight from gene expression data. Achieving reproducible results is a fundamental part of any expression analysis. In this paper, we propose a systematic approach to study the effect of sample sizes on the reproducibility of the results of 10 gene set analysis methods. To do so, we quantity the concept of reproducibility and use real expression datasets of different sizes. Our findings suggest that, as a general pattern, the results of gene set analysis are more reproducible as sample size increases. However, the smallest sample size for achieving reproducible results are variable across gene set analysis methods. Moreover, for some methods, increasing sample size leads to an increase in the number of false positives.

2009 - Molecular pharmaceutics
Parallel synthesis and screening of polymers for nonviral gene delivery.

We describe the parallel synthesis and in vitro evaluation of a cationic polymer library for the discovery of nonviral gene delivery vectors. The library was synthesized based on the ring-opening polymerization reaction between epoxide groups of diglycidyl ethers and the amines of (poly)amines. Parallel screening of soluble library constituents led to the identification of lead polymers with high DNA-binding efficacies. Transfection efficacies of lead polymers were evaluated using PC3-PSMA human prostate cancer cells and murine osteoblasts in the absence and presence of serum. In vitro experiments resulted in the identification of a candidate polymer that demonstrated significantly higher transfection efficacies and lower cytotoxicities than poly(ethyleneimine) (pEI), the current standard for polymeric transfection agents. In addition, polymers that demonstrated moderately higher and comparable transfection efficacies with respect to pEI were also identified. Our results demonstrate that high-throughput synthesis and screening of polymers is a powerful approach for the identification of novel nonviral gene delivery agents.

2020 - Nature machine intelligence
Gaussian Embedding for Large-scale Gene Set Analysis

Gene sets, including protein complexes and signaling pathways, have proliferated greatly, in large part as a result of high-throughput biological data. Leveraging gene sets to gain insight into biological discovery requires computational methods for converting them into a useful form for available machine learning models. Here, we study the problem of embedding gene sets as compact features that are compatible with available machine learning codes. We present Set2Gaussian, a novel network-based gene set embedding approach, which represents each gene set as a multivariate Gaussian distribution rather than a single point in the low-dimensional space, according to the proximity of these genes in a protein-protein interaction network. We demonstrate that Set2Gaussian improves gene set member identification, accurately stratifies tumors, and finds concise gene sets for gene set enrichment analysis. We further show how Set2Gaussian allows us to identify a previously unknown clinical prognostic and predictive subnetwork around NEFM in sarcoma, which we validate in independent cohorts.

2000 - IEEE Network
Scalable Web server clustering technologies

The exponential growth of the Internet, coupled with the increasing popularity of dynamically generated content on the World Wide Web, has created the need for more and faster Web servers capable of serving the over 100 million Internet users. Server clustering has emerged as a promising technique to build scalable Web servers. We examine the seminal work, early products, and a sample of contemporary commercial offerings in the field of transparent Web server clustering. We broadly classify transparent server clustering into three categories.

2009 - Bioinformatics
A novel algorithm for detecting differentially regulated paths based on gene set enrichment analysis

Motivation: Deregulated signaling cascades are known to play a crucial role in many pathogenic processes, among them are tumor initiation and progression. In the recent past, modern experimental te...

2015 - The FEBS Journal
Uncovering major genomic features of essential genes in Bacteria and a methanogenic Archaea

Identification of essential genes is critical to understanding the physiology of a species, proposing novel drug targets and uncovering minimal gene sets required for life. Although essential gene sets of several organisms have been determined using large‐scale mutagenesis techniques, systematic studies addressing their conservation, genomic context and functions remain scant. Here we integrate 17 essential gene sets from genome‐wide in vitro screenings and three gene collections required for growth in vivo, encompassing 15 Bacteria and one Archaea. We refine and generalize important theories proposed using Escherichia coli. Essential genes are typically monogenic and more conserved than nonessential genes. Genes required in vivo are less conserved than those essential in vitro, suggesting that more divergent strategies are deployed when the organism is stressed by the host immune system and unstable nutrient availability. We identified essential analogous pathways that would probably be missed by orthology‐based essentiality prediction strategies. For example, Streptococcus sanguinis carries horizontally transferred isoprenoid biosynthesis genes that are widespread in Archaea. Genes specifically essential in Mycobacterium tuberculosis and Burkholderia pseudomallei are reported as potential drug targets. Moreover, essential genes are not only preferentially located in operons, but also occupy the first position therein, supporting the influence of their regulatory regions in driving transcription of whole operons. Finally, these important genomic features are shared between Bacteria and at least one Archaea, suggesting that high order properties of gene essentiality and genome architecture were probably present in the last universal common ancestor or evolved independently in the prokaryotic domains.

2012 - PloS one
Pathway-Based Evaluation in Early Onset Colorectal Cancer Suggests Focal Adhesion and Immunosuppression along with Epithelial-Mesenchymal Transition

Colorectal cancer (CRC) has one of the highest incidences among all cancers. The majority of CRCs are sporadic cancers that occur in individuals without family histories of CRC or inherited mutations. Unfortunately, whole-genome expression studies of sporadic CRCs are limited. A recent study used microarray techniques to identify a predictor gene set indicative of susceptibility to early-onset CRC. However, the molecular mechanisms of the predictor gene set were not fully investigated in the previous study. To understand the functional roles of the predictor gene set, in the present study we applied a subpathway-based statistical model to the microarray data from the previous study and identified mechanisms that are reasonably associated with the predictor gene set. Interestingly, significant subpathways belonging to 2 KEGG pathways (focal adhesion; natural killer cell-mediated cytotoxicity) were found to be involved in the early-onset CRC patients. We also showed that the 2 pathways were functionally involved in the predictor gene set using a text-mining technique. Entry of a single member of the predictor gene set triggered a focal adhesion pathway, which confers anti-apoptosis in the early-onset CRC patients. Furthermore, intensive inspection of the predictor gene set in terms of the 2 pathways suggested that some entries of the predictor gene set were implicated in immunosuppression along with epithelial-mesenchymal transition (EMT) in the early-onset CRC patients. In addition, we compared our subpathway-based statistical model with a gene set-based statistical model, MIT Gene Set Enrichment Analysis (GSEA). Our method showed better performance than GSEA in the sense that our method was more consistent with a well-known cancer-related pathway set. Thus, the biological suggestion generated by our subpathway-based approach seems quite reasonable and warrants a further experimental study on early-onset CRC in terms of dedifferentiation or differentiation, which is underscored in EMT and immunosuppression.

2009 - Bioinformatics
A flexible two-stage procedure for identifying gene sets that are differentially expressed

MOTIVATION Microarray data analysis has expanded from testing individual genes for differential expression to testing gene sets for differential expression. The tests at the gene set level may focus on multivariate expression changes or on the differential expression of at least one gene in the gene set. These tests may be powerful at detecting subtle changes in expression, but findings at the gene set level need to be examined further to understand whether they are informative and if so how. RESULTS We propose to first test for differential expression at the gene set level but then proceed to test for differential expression of individual genes within discovered gene sets. We introduce the overall false discovery rate (OFDR) as an appropriate error rate to control when testing multiple gene sets and genes. We illustrate the advantage of this procedure over procedures that only test gene sets or individual genes.

2009 - Statistical Methods in Medical Research
Prior biological knowledge-based approaches for the analysis of genome-wide expression profiles using gene sets and pathways

An increasing challenge in analysis of microarray data is how to interpret and gain biological insight of profiles of thousands of genes. This article provides a review of statistical methods for analysis of microarray data by incorporating prior biological knowledge using gene sets and biological pathways, which consist of groups of biologically similar genes. We first discuss issues of individual gene analysis. We compare several methods for analysis of gene sets including over-representation anlaysis, gene set enrichment analysis, principal component analysis, global test and kernel machine. We discuss the assumptions of these methods and their pros and cons. We illustrate these methods by application to a type II diabetes data set.

2008 - BMC Bioinformatics
Microarray-based gene set analysis: a comparison of current methods

BackgroundThe analysis of gene sets has become a popular topic in recent times, with researchers attempting to improve the interpretability and reproducibility of their microarray analyses through the inclusion of supplementary biological information. While a number of options for gene set analysis exist, no consensus has yet been reached regarding which methodology performs best, and under what conditions. The goal of this work was to examine the performance characteristics of a collection of existing gene set analysis methods, on both simulated and real microarray data sets. Of particular interest was the potential utility gained through the incorporation of inter-gene correlation into the analysis process.ResultsEach of six gene set analysis methods was applied to both simulated and publicly available microarray data sets. Overall, the various methodologies were all found to be better at detecting gene sets that moved from non-active (i.e., genes not expressed) to active states (or vice versa), rather than those that simply changed their level of activity. Methods which incorporate correlation structures were found to provide increased ability to detect altered gene sets in some settings.ConclusionBased on the results obtained through the analysis of simulated data, it is clear that the performance of gene set analysis methods is strongly influenced by the features of the data set in question, and that methods which incorporate correlation structures into the analysis process tend to achieve better performance, relative to methods which rely on univariate test statistics.

2022 - Reproductive Biology and Endocrinology : RB&E
Reproductive Biology and Endocrinology Open Access a Cross-study Gene Set Enrichment Analysis Identifies Critical Pathways in Endometriosis

BackgroundEndometriosis is an enigmatic disease. Gene expression profiling of endometriosis has been used in several studies, but few studies went further to classify subtypes of endometriosis based on expression patterns and to identify possible pathways involved in endometriosis. Some of the observed pathways are more inconsistent between the studies, and these candidate pathways presumably only represent a fraction of the pathways involved in endometriosis.MethodsWe applied a standardised microarray preprocessing and gene set enrichment analysis to six independent studies, and demonstrated increased concordance between these gene datasets.ResultsWe find 16 up-regulated and 19 down-regulated pathways common in ovarian endometriosis data sets, 22 up-regulated and one down-regulated pathway common in peritoneal endometriosis data sets. Among them, 12 up-regulated and 1 down-regulated were found consistent between ovarian and peritoneal endometriosis. The main canonical pathways identified are related to immunological and inflammatory disease. Early secretory phase has the most over-represented pathways in the three uterine cycle phases. There are no overlapping significant pathways between the dataset from human endometrial endothelial cells and the datasets from ovarian endometriosis which used whole tissues.ConclusionThe study of complex diseases through pathway analysis is able to highlight genes weakly connected to the phenotype which may be difficult to detect by using classical univariate statistics. By standardised microarray preprocessing and GSEA, we have increased the concordance in identifying many biological mechanisms involved in endometriosis. The identified gene pathways will shed light on the understanding of endometriosis and promote the development of novel therapies.

2004 - Nature Genetics
Reply to "Statistical concerns about the GSEA procedure"

To the editor: Mootha et al.1 propose a statistical method (Gene Set Enrichment Analysis; GSEA) to discern changes in expression levels of sets of genes selected a priori in transcriptional profiling experiments. Although consideration of groups of genes is an interesting strategy, the proposed test statistic may not necessarily determine “...if the members of a given gene set are enriched among the most differentially expressed genes between two classes”1. Situations will probably arise when using GSEA in which genes with the highest values of the difference metric will be ignored solely due to the size of the selected gene sets, unrelated to any biological context of the genes comprising the set. By way of illustration, consider the following hypothetical example. Assume that a given data set consists of three potentially interesting sets of genes S1, S2 and S3, of respective sizes n, 5n and 4n genes, where n is any integer. Assume also that all of the genes in S1 are ranked higher (i.e., they have greater differences in expression) than the genes in S2, which in turn are ranked higher than the genes in S3. The GSEA procedure yields enrichment scores (ES)1 of 3n, 4n and 0 for S1, S2 and S3, respectively. The maximum ES1 is 4n and is attributed to S2. S2 will therefore be singled out as the candidate for further investigation over S1, even though S1 comprises the highest ranked genes. This does not seem reasonable, because S2 has been chosen only by virtue of containing a larger number of genes. In other words, GSEA can be at odds with the picture suggested by the gene ranking. A second observation, using the same illustrative example as above, gives another counterintuitive result. In the absence of a defined third gene set (S3), the ES for S2 = 0 and the ES for S1 remains positive. Therefore, S1, and not S2, is chosen by GSEA, a result opposite to that of the previous scenario. An unusual situation has arisen in which a choice or preference between sets of high ranking is affected simply by the presence or absence of a lower ranking set. The behavior of GSEA can not be dismissed as one of the usual power issues encountered due to noise in data, small sample size or lack of robustness to model assumptions. The simple example outlined here indicates that the power of the test statistic is sensitive to the a priori definition of the hypotheses of interest. These limitations should be clearly understood in applying and interpreting the results of the approach.

2010 - BMC Bioinformatics
Bi-directional gene set enrichment and canonical correlation analysis identify key diet-sensitive pathways and biomarkers of metabolic syndrome

BackgroundCurrently, a number of bioinformatics methods are available to generate appropriate lists of genes from a microarray experiment. While these lists represent an accurate primary analysis of the data, fewer options exist to contextualise those lists. The development and validation of such methods is crucial to the wider application of microarray technology in the clinical setting. Two key challenges in clinical bioinformatics involve appropriate statistical modelling of dynamic transcriptomic changes, and extraction of clinically relevant meaning from very large datasets.ResultsHere, we apply an approach to gene set enrichment analysis that allows for detection of bi-directional enrichment within a gene set. Furthermore, we apply canonical correlation analysis and Fisher's exact test, using plasma marker data with known clinical relevance to aid identification of the most important gene and pathway changes in our transcriptomic dataset. After a 28-day dietary intervention with high-CLA beef, a range of plasma markers indicated a marked improvement in the metabolic health of genetically obese mice. Tissue transcriptomic profiles indicated that the effects were most dramatic in liver (1270 genes significantly changed; p < 0.05), followed by muscle (601 genes) and adipose (16 genes). Results from modified GSEA showed that the high-CLA beef diet affected diverse biological processes across the three tissues, and that the majority of pathway changes reached significance only with the bi-directional test. Combining the liver tissue microarray results with plasma marker data revealed 110 CLA-sensitive genes showing strong canonical correlation with one or more plasma markers of metabolic health, and 9 significantly overrepresented pathways among this set; each of these pathways was also significantly changed by the high-CLA diet. Closer inspection of two of these pathways - selenoamino acid metabolism and steroid biosynthesis - illustrated clear diet-sensitive changes in constituent genes, as well as strong correlations between gene expression and plasma markers of metabolic syndrome independent of the dietary effect.ConclusionBi-directional gene set enrichment analysis more accurately reflects dynamic regulatory behaviour in biochemical pathways, and as such highlighted biologically relevant changes that were not detected using a traditional approach. In such cases where transcriptomic response to treatment is exceptionally large, canonical correlation analysis in conjunction with Fisher's exact test highlights the subset of pathways showing strongest correlation with the clinical markers of interest. In this case, we have identified selenoamino acid metabolism and steroid biosynthesis as key pathways mediating the observed relationship between metabolic health and high-CLA beef. These results indicate that this type of analysis has the potential to generate novel transcriptome-based biomarkers of disease.

2010 - PloS one
Enrichment Map: A Network-Based Method for Gene-Set Enrichment Visualization and Interpretation

Background Gene-set enrichment analysis is a useful technique to help functionally characterize large gene lists, such as the results of gene expression experiments. This technique finds functionally coherent gene-sets, such as pathways, that are statistically over-represented in a given gene list. Ideally, the number of resulting sets is smaller than the number of genes in the list, thus simplifying interpretation. However, the increasing number and redundancy of gene-sets used by many current enrichment analysis software works against this ideal. Principal Findings To overcome gene-set redundancy and help in the interpretation of large gene lists, we developed “Enrichment Map”, a network-based visualization method for gene-set enrichment results. Gene-sets are organized in a network, where each set is a node and edges represent gene overlap between sets. Automated network layout groups related gene-sets into network clusters, enabling the user to quickly identify the major enriched functional themes and more easily interpret the enrichment results. Conclusions Enrichment Map is a significant advance in the interpretation of enrichment analysis. Any research project that generates a list of genes can take advantage of this visualization framework. Enrichment Map is implemented as a freely available and user friendly plug-in for the Cytoscape network visualization software (http://baderlab.org/Software/EnrichmentMap/).

2013 - Nucleic Acids Research
WEB-based GEne SeT AnaLysis Toolkit (WebGestalt): update 2013

Functional enrichment analysis is an essential task for the interpretation of gene lists derived from large-scale genetic, transcriptomic and proteomic studies. WebGestalt (WEB-based GEne SeT AnaLysis Toolkit) has become one of the popular software tools in this field since its publication in 2005. For the last 7 years, WebGestalt data holdings have grown substantially to satisfy the requirements of users from different research areas. The current version of WebGestalt supports 8 organisms and 201 gene identifiers from various databases and different technology platforms, making it directly available to the fast growing omics community. Meanwhile, by integrating functional categories derived from centrally and publicly curated databases as well as computational analyses, WebGestalt has significantly increased the coverage of functional categories in various biological contexts including Gene Ontology, pathway, network module, gene–phenotype association, gene–disease association, gene–drug association and chromosomal location, leading to a total of 78 612 functional categories. Finally, new interactive features, such as pathway map, hierarchical network visualization and phenotype ontology visualization have been added to WebGestalt to help users better understand the enrichment results. WebGestalt can be freely accessed through http://www.webgestalt.org or http://bioinfo.vanderbilt.edu/webgestalt/.

2005 - Nucleic Acids Research
AUGUSTUS: a web server for gene prediction in eukaryotes that allows user-defined constraints

We present a WWW server for AUGUSTUS, a software for gene prediction in eukaryotic genomic sequences that is based on a generalized hidden Markov model, a probabilistic model of a sequence and its gene structure. The web server allows the user to impose constraints on the predicted gene structure. A constraint can specify the position of a splice site, a translation initiation site or a stop codon. Furthermore, it is possible to specify the position of known exons and intervals that are known to be exonic or intronic sequence. The number of constraints is arbitrary and constraints can be combined in order to pin down larger parts of the predicted gene structure. The result then is the most likely gene structure that complies with all given user constraints, if such a gene structure exists. The specification of constraints is useful when part of the gene structure is known, e.g. by expressed sequence tag or protein sequence alignments, or if the user wants to change the default prediction. The web interface and the downloadable stand-alone program are available free of charge at .

2012 - Bioinformatics
EnrichNet: network-based gene set enrichment analysis

Motivation: Assessing functional associations between an experimentally derived gene or protein set of interest and a database of known gene/protein sets is a common task in the analysis of large-scale functional genomics data. For this purpose, a frequently used approach is to apply an over-representation-based enrichment analysis. However, this approach has four drawbacks: (i) it can only score functional associations of overlapping gene/proteins sets; (ii) it disregards genes with missing annotations; (iii) it does not take into account the network structure of physical interactions between the gene/protein sets of interest and (iv) tissue-specific gene/protein set associations cannot be recognized. Results: To address these limitations, we introduce an integrative analysis approach and web-application called EnrichNet. It combines a novel graph-based statistic with an interactive sub-network visualization to accomplish two complementary goals: improving the prioritization of putative functional gene/protein set associations by exploiting information from molecular interaction networks and tissue-specific gene expression data and enabling a direct biological interpretation of the results. By using the approach to analyse sets of genes with known involvement in human diseases, new pathway associations are identified, reflecting a dense sub-network of interactions between their corresponding proteins. Availability: EnrichNet is freely available at http://www.enrichnet.org. Contact: Natalio.Krasnogor@nottingham.ac.uk, reinhard.schneider@uni.lu or avalencia@cnio.es Supplementary Information: Supplementary data are available at Bioinformatics Online.

Enrichr: a comprehensive gene set enrichment analysis web server 2016 update

Andrew D. Rouillard
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Avi Ma'ayan
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Matthew R. Jones
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Kathleen M. Jagodnik
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Nicolas F. Fernandez
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Zichen Wang
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Alexander Lachmann
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Gregory W. Gundersen
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Qiaonan Duan
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Simon Koplev
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Caroline D. Monteiro
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Michael G. McDermott
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Sherry L. Jenkins
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Maxim V. Kuleshov
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Avi Ma’ayan
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Alexander Lachmann
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Qiaonan Duan
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Zichen Wang
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A. Rouillard
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Simon Koplev
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Matthew R. Jones
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K. Jagodnik
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S. L. Jenkins

Abstract:Enrichment analysis is a popular method for analyzing gene sets generated by genome-wide experiments. Here we present a significant update to one of the tools in this domain called Enrichr. Enrichr currently contains a large collection of diverse gene set libraries available for analysis and download. In total, Enrichr currently contains 180 184 annotated gene sets from 102 gene set libraries. New features have been added to Enrichr including the ability to submit fuzzy sets, upload BED files, improved application programming interface and visualization of the results as clustergrams. Overall, Enrichr is a comprehensive resource for curated gene sets and a search engine that accumulates biological knowledge for further biological discoveries. Enrichr is freely available at: http://amp.pharm.mssm.edu/Enrichr.

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eLife
2019
Graph Algorithms for Condensing and Consolidating Gene Set Analysis Results*
Molecular & Cellular Proteomics
2019
Connectivity Analyses of Bioenergetic Changes in Schizophrenia: Identification of Novel Treatments
Molecular Neurobiology
2018
Shifts in myeloarchitecture characterise adolescent development of cortical gradients
eLife
2019
PaccMann: Prediction of anticancer compound sensitivity with multi-modal attention-based neural networks
ArXiv
2018
SCENIC: Single-cell regulatory network inference and clustering
Nature Methods
2017
Cross-species systems analysis identifies gene networks differentially altered by sleep loss and depression
Science Advances
2018
OmicsMapNet: Transforming omics data to take advantage of Deep Convolutional Neural Network for discovery
ArXiv
2018
Single-cell regulatory network inference and clustering from high-dimensional sequencing data
2019 IEEE International Conference on Big Data (Big Data)
2019
Inter-individual body mass variations relate to fractionated functional brain hierarchies
Communications Biology
2020