Assessing the gain of biological data integration in gene networks inference.

ABSTRACT:

Background

A current challenge in gene annotation is to define the gene function in the context of the network of relationships instead of using single genes. The inference of gene networks (GNs) has emerged as an approach to better understand the biology of the system and to study how several components of this network interact with each other and keep their functions stable. However, in general there is no sufficient data to accurately recover the GNs from their expression levels leading to the curse of dimensionality, in which the number of variables is higher than samples. One way to mitigate this problem is to integrate biological data instead of using only the expression profiles in the inference process. Nowadays, the use of several biological information in inference method

SUBMITTER: Vicente FF 

PROVIDER: S-EPMC3481449 | biostudies-literature | 2012

REPOSITORIES: biostudies-literature