The identification of informative genes from multiple datasets with increasing complexity.

ABSTRACT:

Background

In microarray data analysis, factors such as data quality, biological variation, and the increasingly multi-layered nature of more complex biological systems complicates the modelling of regulatory networks that can represent and capture the interactions among genes. We believe that the use of multiple datasets derived from related biological systems leads to more robust models. Therefore, we developed a novel framework for modelling regulatory networks that involves training and evaluation on independent datasets. Our approach includes the following steps: (1) ordering the datasets based on their level of noise and informativeness; (2) selection of a Bayesian classifier with an appropriate level of complexity by evaluation of predictive performance on independent data s

SUBMITTER: Anvar SY 

PROVIDER: S-EPMC2822754 | biostudies-literature | 2010 Jan

REPOSITORIES: biostudies-literature