Computational and Mathematical Methods to Study the Complexity of Regulatory Networks in Mammalian Cells

The Ma’ayan Laboratory 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.

Our research team 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. Read More

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• BD2K Centers Open Doors to Discovery
• Big Data and Drugs: BD2K Centers Solidify Emerging Approaches

 Featured Publications

• Developing a Framework for Digital Objects in the Big Data to Knowledge (BD2K) Commons: Report from the Commons Framework Pilots Workshop
• Differential Cytokine Contributions of Perivascular Haematopoietic Stem Cell Niches
• GEN3VA: Aggregation and Analysis of Gene Expression Signatures from Related Studies
• 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