Article
Choosing where to look next in a mutation sequence space: Active Learning of informative p53 cancer rescue mutants.
Bioinformatics (Oxford, England) - 1 Jul 2007
Danziger Samuel A, Zeng Jue, Wang Ying, Brachmann Rainer K, Lathrop Richard H
Abstract excerpt
MOTIVATION: Many biomedical projects would benefit from reducing the time and expense of in vitro experimentation by using computer models for in silico predictions. These models may help determine which expensive biological data are most useful to acquire next. Active Learning techniques for choosing the most informative data enable biologists and computer scientists to optimize experimental data choices for...
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