PNN for Molecular Level Selection Detection.
Contemporary population genetics has developed several statistical tests designed for the detection of natural selection at the molecular level. However, the appropriate interpretation of the test results is often hard. This is because such factors as population growth, migration, and recombination can produce similar values for some of these tests. To overcome these difficulties, the author has proposed a so-called multi-null methodology, and he has used it in search of natural selection in ATM, RECQL, WRN, and BLM, i.e., in four human familial cancer genes. However, this methodology is not appropriate for fast detection because of the long-lasting computer simulations required for estimating critical values under nonclassical null hypotheses. Here the author presents the results of another study based on the application of probabilistic neural networks for the detection of natural selection at the molecular level. The advantage of the proposed method is that it not so time-consuming and, because of the good recognition abilities of probabilistic neural networks, it gives low decision error levels in cross validation.
KeywordsNatural Selection Single Nucleotide Polymorphism Ataxia Telangiectasia Mutate Probabilistic Neural Network Ataxia Telangiectasia
The author would like to acknowledge his financial support under the habilitation grant number BW/RGH-5/Rau-0/2007, under SUT statutory activities BK2007, and under MNiSW grant number 3T11F 010 29. Also the author would like to thank Prof. M. Kimmel from the Department of Statistics at Rice University in Houston TX, USA, for advice and long discussions concerning the statistical and biological aspects of the research using non-neutrality tests for the detection of natural selection operating at the molecular level.
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