Predicting positive p53 cancer rescue regions using Most Informative Positive (MIP) active learning.

ABSTRACT: Many protein engineering problems involve finding mutations that produce proteins with a particular function. Computational active learning is an attractive approach to discover desired biological activities. Traditional active learning techniques have been optimized to iteratively improve classifier accuracy, not to quickly discover biologically significant results. We report here a novel active learning technique, Most Informative Positive (MIP), which is tailored to biological problems because it seeks novel and informative positive results. MIP active learning differs from traditional active learning methods in two ways: (1) it preferentially seeks Positive (functionally active) examples; and (2) it may be effectively extended to select gene regions suitable for high throughput combina

SUBMITTER: Danziger SA 

PROVIDER: S-EPMC2742196 | biostudies-literature | 2009 Sep

REPOSITORIES: biostudies-literature