4.7 Article

Functional census of mutation sequence spaces: The example of p53 cancer rescue mutants

Publisher

IEEE COMPUTER SOC
DOI: 10.1109/TCBB.2006.22

Keywords

biology and genetics; feature extraction or construction; machine learning; medicine and science

Funding

  1. NCI NIH HHS [R01 CA081511, R01 CA112560, CA-112560, R01 CA112560-01A1, CA81511] Funding Source: Medline
  2. NLM NIH HHS [LM-07443-01, T15 LM007443] Funding Source: Medline

Ask authors/readers for more resources

Many biomedical problems relate to mutant functional properties across a sequence space of interest, e. g., flu, cancer, and HIV. Detailed knowledge of mutant properties and function improves medical treatment and prevention. A functional census of p53 cancer rescue mutants would aid the search for cancer treatments from p53 mutant rescue. We devised a general methodology for conducting a functional census of a mutation sequence space by choosing informative mutants early. The methodology was tested in a double-blind predictive test on the functional rescue property of 71 novel putative p53 cancer rescue mutants iteratively predicted in sets of three ( 24 iterations). The first double-blind 15-point moving accuracy was 47 percent and the last was 86 percent; r = 0.01 before an epiphanic 16th iteration and r = 0.92 afterward. Useful mutants were chosen early ( overall r = 0.80). Code and data are freely available (http://www.igb.uci.edu/research/research.html, corresponding authors: R. H. L. for computation and R. K. B. for biology).

Authors

I am an author on this paper
Click your name to claim this paper and add it to your profile.

Reviews

Primary Rating

4.7
Not enough ratings

Secondary Ratings

Novelty
-
Significance
-
Scientific rigor
-
Rate this paper

Recommended

No Data Available
No Data Available