Computational and Mathematical Methods to Study the Complexity of Regulatory Networks in Mammalian Cells
The Ma’ayan Laboratory applies graph-theory algorithms, machine-learning techniques and dynamical modeling to study how intracellular regulatory systems function as networks to control cellular processes such as differentiation, apoptosis and proliferation.
Our research team develops software systems to help experimental biologists form novel hypotheses from high-throughput data, and develop theories about the structure and function of regulatory networks in mammalian systems. Read More
- Extraction and Analysis of Signatures from the Gene Expression Omnibus by the Crowd
- L1000CDS2: LINCS L1000 Characteristic Direction Signatures Search Engine
- An Open RNA-Seq Data Analysis Pipeline Tutorial with an Example of Reprocessing Data from a Recent Zika Virus Study
- The Harmonizome: A Collection of Processed Datasets Gathered to Serve and Mine Knowledge about Genes and Proteins
- Enrichr: A Comprehensive Gene Set Enrichment Analysis Web Server 2016 Update
- Drug-induced Adverse Events Prediction with the LINCS L1000 Data
In the News
Crowdsourcing for Scientific Discovery: Mount Sinai Researchers Find Novel Ways to Analyze Data for Drug and Target Discovery
Researchers in the Ma’ayan Laboratory have crowdsourced the annotation and analysis of a large number of gene expression profiles from the Gene Expression Omnibus (GEO). More than 70 volunteers from 25 countries helped the team analyze the data, enabling the identification of new associations between genes, diseases, and drugs. This study was published in the journal Nature Communications.
Read the full press release here.
Citation: Wang et al. Extraction and analysis of signatures from the Gene Expression Omnibus by the crowd. Nature Communications 2016 Sep 26;7:12846.