Issue No. 02 - April-June (2006 vol. 3)
DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/TCBB.2006.22
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).
Biology and genetics, feature extraction or construction, machine learning, medicine and science.
Q. Lu et al., "Functional Census of Mutation Sequence Spaces: The Example of p53 Cancer Rescue Mutants," in IEEE/ACM Transactions on Computational Biology and Bioinformatics, vol. 3, no. , pp. 114-125, 2006.