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Bayesian Classification and Non‐Bayesian Label Estimation via EM Algorithm to Identify Differentially Expressed Genes: a Comparative Study
Author(s) -
Antunes Marília,
Sousa Lisete
Publication year - 2008
Publication title -
biometrical journal
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.108
H-Index - 63
eISSN - 1521-4036
pISSN - 0323-3847
DOI - 10.1002/bimj.200710468
Subject(s) - categorical variable , bayesian probability , mathematics , conditional probability , gene , bayes' theorem , dna microarray , pattern recognition (psychology) , gene expression , statistics , algorithm , computational biology , biology , artificial intelligence , computer science , genetics
Gene classification problem is studied considering the ratio of gene expression levels, X , in two‐channel microarrays and a non‐observed categorical variable indicating how differentially expressed the gene is: non differentially expressed , down‐regulated or up‐regulated . Supposing X from a mixture of Gamma distributions, two methods are proposed and results are compared. The first method is based on an hierarchical Bayesian model. The conditional predictive probability of a gene to belong to each group is calculated and the gene is assigned to the group for which this conditional probability is higher. The second method uses EM algorithm to estimate the most likely group label for each gene, that is, to assign the gene to the group which contains it with the higher estimated probability. (© 2008 WILEY‐VCH Verlag GmbH & Co. KGaA, Weinheim)