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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 (, corresponding authors: R.H.L. for computation and R.K.B. for biology).
Biology and genetics, feature extraction or construction, machine learning, medicine and science.
Qiang Lu, Hiroto Saigo, Lawrence R. Dearth, S. Joshua Swamidass, Rainer K. Brachmann, Samuel A. Danziger, Jianlin Cheng, Jonathan H. Chen, Richard H. Lathrop, Pierre Baldi, Ray Luo, Vinh P. Hoang, Jue Zeng, "Functional Census of Mutation Sequence Spaces: The Example of p53 Cancer Rescue Mutants", IEEE/ACM Transactions on Computational Biology and Bioinformatics, vol. 3, no. , pp. 114-125, April-June 2006, doi:10.1109/TCBB.2006.22
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