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Choosing where to look next in a mutation sequence space: Active Learning of informative p53 cancer rescue mutants

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 choosi...

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Autori principali: Danziger, Samuel A., Zeng, Jue, Wang, Ying, Brachmann, Rainer K., Lathrop, Richard H.
Natura: Artigo
Lingua:Inglês
Pubblicazione: 2007
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Accesso online:https://ncbi.nlm.nih.gov/pmc/articles/PMC2811495/
https://ncbi.nlm.nih.gov/pubmed/17646286
https://ncbi.nlm.nih.govhttp://dx.doi.org/10.1093/bioinformatics/btm166
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