Comput Biol Chem - Multi objective SNP selection using pareto optimality.

Tópicos

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Resumo

Biomarker discovery is a challenging task of bioinformatics especially when targeting high dimensional problems such as SNP (single nucleotide polymorphism) datasets. Various types of feature selection methods can be applied to accomplish this task. Typically, using features versus class labels of samples in the training dataset, these methods aim at selecting feature subsets with maximal classification accuracies. Although finding such class-discriminative features is crucial, selection of relevant SNPs for maximizing other properties that exist in the nature of population genetics such as the correlation between genetic diversity and geographical distance of ethnic groups can also be equally important. In this work, a methodology using a multi objective optimization technique called Pareto Optimal is utilized for selecting SNP subsets offering both high classification accuracy and correlation between genomic and geographical distances. In this method, discriminatory power of an SNP is determined using mutual information and its contribution to the genomic-geographical correlation is estimated using its loadings on principal components. Combining these objectives, the proposed method identifies SNP subsets that can better discriminate ethnic groups than those obtained with sole mutual information and yield higher correlation than those obtained with sole principal components on the Human Genome Diversity Project (HGDP) SNP dataset.

Resumo Limpo

biomark discoveri challeng task bioinformat especi target high dimension problem snp singl nucleotid polymorph dataset various type featur select method can appli accomplish task typic use featur versus class label sampl train dataset method aim select featur subset maxim classif accuraci although find classdiscrimin featur crucial select relev snps maxim properti exist natur popul genet correl genet divers geograph distanc ethnic group can also equal import work methodolog use multi object optim techniqu call pareto optim util select snp subset offer high classif accuraci correl genom geograph distanc method discriminatori power snp determin use mutual inform contribut genomicgeograph correl estim use load princip compon combin object propos method identifi snp subset can better discrimin ethnic group obtain sole mutual inform yield higher correl obtain sole princip compon human genom divers project hgdp snp dataset

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