
Avi Ma'ayan, PhD
About Me
Dr. 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
Language
Position
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 Virology
Multi-Disciplinary Training Areas
Artificial Intelligence and Emerging Technologies in Medicine [AIET], Disease Mechanisms and Therapeutics (DMT), Genetics and Genomic Sciences [GGS]