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

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