A Bayesian framework for combining heterogeneous data sources for gene function prediction (in Saccharomyces cerevisiae).

ABSTRACT: Genomic sequencing is no longer a novelty, but gene function annotation remains a key challenge in modern biology. A variety of functional genomics experimental techniques are available, from classic methods such as affinity precipitation to advanced high-throughput techniques such as gene expression microarrays. In the future, more disparate methods will be developed, further increasing the need for integrated computational analysis of data generated by these studies. We address this problem with MAGIC (Multisource Association of Genes by Integration of Clusters), a general framework that uses formal Bayesian reasoning to integrate heterogeneous types of high-throughput biological data (such as large-scale two-hybrid screens and multiple microarray analyses) for accurate gene function pre

SUBMITTER: Troyanskaya OG 

PROVIDER: S-EPMC166232 | biostudies-literature | 2003 Jul

REPOSITORIES: biostudies-literature