Discovering collectively informative descriptors from high-throughput experiments.

ABSTRACT:

Background

Improvements in high-throughput technology and its increasing use have led to the generation of many highly complex datasets that often address similar biological questions. Combining information from these studies can increase the reliability and generalizability of results and also yield new insights that guide future research.

Results

This paper describes a novel algorithm called BLANKET for symmetric analysis of two experiments that assess informativeness of descriptors. The experiments are required to be related only in that their descriptor sets intersect substantially and their definitions of case and control are consistent. From resulting lists of n descriptors ranked by informativeness, BLANKET determines shortlists of descriptors from each experiment, ge

SUBMITTER: Jeffries CD 

PROVIDER: S-EPMC2813853 | biostudies-literature | 2009 Dec

REPOSITORIES: biostudies-literature