Discovering Study-Specific Gene Regulatory Networks

Discovering Study-Specific Gene Regulatory Networks

Microarrays are commonly used in biology because of their ability to simultaneously measure thousands of genes under different conditions. Due to their structure, typically containing a high amount of variables but far fewer samples, scalable network analysis techniques are often employed. In particular, consensus approaches have been recently used that combine multiple microarray studies in or...

Module Networks: Discovering Regulatory Modules and their Condition Specific Regulators from Gene Expression Data

Modules and their Condition Specific Regulators from Gene Expression Data Eran Segal , Michael Shapira, Aviv Regev, Dana Pe’er David Botstein, Daphne Koller , Nir Friedman Much of a cell's activity is organized as a network of interacting modules: sets of genes co-regulated to respond to different conditions. We present a probabilistic method for discovering regulatory modules from gene express...

Computational Methods of Discovering Gene Modules and Constructing Regulatory Networks

Clustering Context-Specific Gene Regulatory Networks

Gene regulatory networks (GRNs) learned from high throughput genomic data are often hard to visualize due to the large number of nodes and edges involved, rendering them difficult to appreciate. This becomes an important issue when modular structures are inherent in the inferred networks, such as in the recently proposed context-specific GRNs.(12) In this study, we investigate the application o...

Toward discovering disease-specific gene networks from online literature

Many human diseases are the result of abnormal interactions between multiple genes instead of single gene mutations. Discovering the interactions between these genes and their relationships to human diseases is critical for understanding mechanisms of diseases and helping design effective therapies. Valuable experimental evidence from years of industrious research by biologists can be used to h...