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
Recent News
Feature Article in GEN
Gene Expression’s Big Rethink
Highlight on our open-source bioinformatics pipeline to extract knowledge from typical RNA-Seq studies and generate interactive principal component analysis (PCA) plots. The PCA plot shown here was generated using Gene Expression Omnibus/Sequence Read Archive data, which represents ~55,000 RNA-Seq human samples. Colors reflect the results of text searches on the metadata associated with each sample.
Guest Editorial
Big (Data) Changes
Improving the Evaluation of Biomedical Academic Software Development Projects
Article in Biomedical Computation Review by Avi Ma’ayan, PhD
Big Data Highlight
The Harmonizome
A Prototype for Integrated Datasets
Article in Biomedical Computation Review by Katharine Miller
Featured Publications
- 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