Avi Ma'ayan, PhD
- PROFESSOR | Pharmacological Sciences
Research Topics:
Addiction, Aging, Bioinformatics, Biomedical Sciences, Biostatistics, Cancer, Computational Biology, Diabetes, Drug Design and Discovery, Gene Expressions, Gene Regulation, Genetics, Genomics, Kidney, Mass Spectrometry, Mathematical Modeling of Biomedical Systems, Mathematical and Computational Biology, Personalized Medicine, Pharmacogenomics, Pharmacology, Protein Complexes, Protein Kinases, Proteomics, Reprogramming, Signal Transduction, Stem Cells, Systems Biology, Systems Pharmacology, Technology & Innovation, Theoretical Biology, Transcription Factors, Viruses and VirologyDr. Ma’ayan is a Mount Sinai Endowed Professor in Bioinformatics, Professor in the Department of Pharmacological Sciences, Director of the Mount Sinai Center for Bioinformatics, and a faculty member of the Icahn Genomics Institute. Dr. Ma'ayan is also Principal Investigator of the NIH-funded Mount Sinai Knowledge Management Center for Illuminating the Druggable Genome and Mount Sinai Proteogenomic Data Analysis Center. The Ma'ayan Laboratory applies computational and mathematical methods to study the complexity of regulatory networks in mammalian cells. His research team applies machine learning and other statistical mining techniques to study how intracellular regulatory systems function as networks to control cellular processes such as differentiation, dedifferentiation, apoptosis and proliferation. The Ma'ayan Laboratory develops software systems to help experimental biologists form novel hypotheses from high-throughput data, while aiming to better understand the structure and function of regulatory networks in mammalian cellular and multi-cellular systems.
Avi Ma'ayan's Publications on PubMed | Google Scholar | ResearchGate
Featured Software Tools Developed by the Ma'ayan Laboratory:
- Appyters: Collection of web-based applications to execute bioinformatics workflows
- Drugmonizome: Web portal for querying annotated sets of drugs and small molecules
- KEA3: Kinase enrichment analysis version 3
- COVID-19 Drug and Gene Set Library: Collection of drug and gene sets from COVID-19 research community
- Geneshot: Search engine for ranking genes from arbitrary text queries
- ChEA3: ChIP-X enrichment analysis
- DGB: Ranks drugs to modulate genes based on transcriptomic signatures
- BioJupies: Automatically generates RNA-seq data analysis notebooks
- X2K Web: Linking expression signatures to upstream cell signling networks
- ARCHS4: All RNA-seq and ChIP-seq signature search space
- L1000FWD: Large-scale visualization of drug-induced transcriptomic signatures
- Clustergrammer: Visualization and analyis tool for high-dimensional biological data
- L1000CDS2: L1000 Characteristic DIrection signature search engine
- Harmonizome: A biological knowledge engine
- Enrichr: Gene-list enrichment analysis tool
For a complete list of our software tools, databases and datasets, please visit our Resources page.
NIH-funded Centers:
- Mount Sinai's Proteogenomic Data Analysis Center (PGDAC)
- Mount Sinai's Knowledge Management Center for Illuminating the Druggable Genome (KMC-IDG)
- Data Coordination and Integration Center (DCIC) for the LINCS Consortium (2014-2022)
In the News:
- Genes to Potentially Diagnose Long-Term Lyme Disease Identified
- Mount Sinai Designated as National Cancer Institute Proteogenomics Data Analysis Center
- Mount Sinai Lab Creates Shared Database to Help Scientists Find Drugs That Can Be Used to Treat COVID-19
- Ten Renowned Mount Sinai Faculty Members Honored at Convocation
- Mount Sinai Researchers Develop Software to Measure the Findability, Accessibility, Interoperability, and Reusability of Biomedical Digital Research Objects
- Mount Sinai Researchers Develop Tool that Analyzes Biomedical Data within Minutes
- Mount Sinai Researchers Receive NIH Grant to Develop New Ways to Share and Reuse Research Data
- Students Harness Big Data to Help Solve Medical Challenges
- Crowdsourcing for Scientific Discovery
- Genetics: Big Hopes for Big Data
Multi-Disciplinary Training Areas
Artificial Intelligence and Emerging Technologies in Medicine [AIET], Genetics and Genomic Sciences [GGS], Pharmacology and Therapeutics Discovery [PTD]Education
BSc, Fairleigh Dickinson University
MS, Fairleigh Dickinson University
PhD, Mount Sinai School of Medicine
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2020
Mount Sinai Graduate School Alumni Award -
2013
Irma T. Hirschl Career Scientist Award -
2011
Dr. Harold and Golden Lamport Research Award -
2006
Doctoral Dissertation Award in the Graduate School of Biological Sciences -
2006
Graduate School of Biological Sciences Award for Research Achievement
Systems Biology, Systems Pharmacology, Biomedical Big Data, Bioinformatics, Computational Biology, Data-Mining, Software Engineering, Network Analysis
Research Team:
Program Director: Sherry Jenkins, MS
Research Assistant Professor: Alexander Lachmann, PhD
Data Scientist: Daniel Clarke, MS
Bioinformaticians: John Erol Evangelista, MS; Sherry Xie, BS
Bioinformatics Software Engineers: Nasheath Ahmed, BS, AB; Ido Diamant, BS; Giacomo Marino, ScB, AB
Systems Analyst: Heesu Kim, MBA
MD Student: Vivian Utti, BS
2022 Undergrad Research Trainees: Clara Chen, Sophia Colmenares, Eden Deng, Lauren Druz, Reid Fleishman, Sophie Goldman, Jason Han, Cole Heine, David Lewis, Nhi Nguyen, Hannah Qu, Derek Wang
Summary of Research Studies:
Advances in high-throughput experimental molecular biology are allowing us to elucidate the molecular mechanisms of mammalian cell regulation with ever-increasing detail. However, the potential gains from these advances are often not fully realized since high-throughput techniques often produce more data than our current ability to adequately organize, model and visualize. A particular challenge is encountered when attempting to integrate several high-dimensional datasets from multiple types of high- and low-throughput experimental techniques applied to study mammalian cells.
For the purpose of organizing, visualizing, analyzing and modeling data from such sources we develop computational approaches which can assist experimental systems-biologists to form rational hypotheses for further experimentation. We analyze high-dimensional data collected for projects integrating results from multiple layers of regulation (genomics, transcriptomics and proteomics). In addition to our research efforts, we also develop software so that our methodologies can reach and impact the Big Data biomedical research community. Below are some of the software tools we have developed:
1) Enrichr is a gene set enrichment analysis tool that includes one of the largest collections of annotated gene sets: 298,481 gene sets organized into 172 gene set libraries. Enrichr provides visualization of enrichment results as bar graphs, tables, canvases and networks. Enrichment is computed by three different methods and users can save and share their lists and results with a single click. Articles describing the initial and updated versions of the software were published in BMC Bioinformatics and Nucleic Acids Research. PMID: 23586463 and PMID: 27141961
2) GEO2Enrichr is a browser extension and a web application for extracting differentially expressed gene sets from GEO and analyzing those sets with Enrichr and other tools. GEO2Enrichradds JavaScript code to GEO web-pages; this code scrapes user selected accession numbers and metadata, and then, with one click, users can submit this information to a web-server application that downloads the SOFT files, parses, cleans and normalizes the data, identifies the differentially expressed genes, and then pipes the resulting gene lists to several downstream analysis tools. An article describing the initial version of the software was published in Bioinformatics. PMID: 25971742
3) L1000CDS2 and Drug Pair Seeker (DPS) are two tools that use the Connectivity Map gene expression datasets, including the new version that utilizes the L1000 technology, to predict single and pairs of drugs that can either mimic or reverse gene expression given signatures of differentially expressed genes. Both tools use novel algorithms developed by the Ma’ayan Laboratory to prioritize drugs and small molecules. A detailed description of Drug Pair Seeker and its application to kidney disease can be found in publication in the journal JSAN. PMID: 23559582. An article describing L1000CDS2 was published in NPJ Systems Biology and Applications. PMID: 28413689
4) ChIP-X Enrichment Analysis (ChEA) database contains manually extracted datasets of transcription-factor/target-gene interactions from over 100 experiments such as ChIP-chip, ChIP-seq, ChIP-PET applied to mammalian cells. We use the database to analyze mRNA expression data where we perform gene-list enrichment analysis as the prior biological knowledge gene-list library. The system is delivered as web-based interactive software. With this software users can input lists of mammalian genes for which the program computes over-representation of transcription factor targets from the ChEA database. An article describing the system has been published in the journal Bioinformatics. PMID: 20709693
5) Kinase Enrichment Analysis (KEA) is a web-based tool with an underlying database providing users with the ability to link lists of mammalian proteins/genes with the kinases that phosphorylate them. The system draws from several available kinase–substrate databases to compute kinase enrichment probability based on the distribution of kinase–substrate proportions in the background kinase–substrate database compared with kinases found to be associated with an input list of genes/proteins. An article describing the system has been published in the journal Bioinformatics. PMID: 19176546
6) Expression2Kinases (X2K) is a software tool that integrates and upgrades the functionality of ChEA, Genes2Networks, KEA and Lists2Networks into one platform and computational pipeline. Given a list of differentially expressed genes, the software identified upstream transcription factors using the software and database ChEA; X2K then connects the top identified transcription factors with Genes2Networks using databases of known protein-protein interactions; the resultant subnetwork is then entered into KEA for kinase enrichment analysis. X2K also includes all the functions for enrichment analysis available within Lists2Networks. An article describing the system has been published in the journal Bioinformatics. PMID: 22080467 and PMID: 29800326
We apply these and other computational methods for the analysis of data from a variety of projects with our collaborators. The results from our analyses produce concrete suggestions and predictions for further functional experiments. The predictions are tested by our collaborators and our analyses methods are delivered as software tools and databases for the systems biology research community.
For more information, please visit the Ma'ayan Laboratory website.
Physicians and scientists on the faculty of the Icahn School of Medicine at Mount Sinai often interact with pharmaceutical, device and biotechnology companies to improve patient care, develop new therapies and achieve scientific breakthroughs. In order to promote an ethical and transparent environment for conducting research, providing clinical care and teaching, Mount Sinai requires that salaried faculty inform the School of their relationships with such companies.
Below are financial relationships with industry reported by Dr. Ma'ayan during 2022 and/or 2023. Please note that this information may differ from information posted on corporate sites due to timing or classification differences.
Equity (Stock or stock options valued at greater than 5% ownership of a publicly traded company or equity of any value in a privately held company)
- Elucidata
Industry-Sponsored Lectures: MSSM faculty occasionally give lectures at events sponsored by industry, but only if the events are free of any marketing purpose.
- Stanford University
Other Activities: Examples include, but are not limited to, committee participation, data safety monitoring board (DSMB) membership.
- John Wiley and Sons, Inc.; National Institutes of Health (NIH)
Mount Sinai's faculty policies relating to faculty collaboration with industry are posted on our website. Patients may wish to ask their physician about the activities they perform for companies.