title: [Machine learning study of DNA binding by transcription factors from the LacI family].
abstract: We studied 1372 LacI-family transcription factors and their 4484 DNA binding sites using machine learning algorithms and feature selection techniques. The Naive Bayes classifier and Logistic Regression were used to predict binding sites given transcription factor sequences and to classify factor-site pairs on binding and non-binding ones. Prediction accuracy was estimated using 10-fold cross-validation. Experiments showed that the best prediction of nucleotide densities at selected site positions is obtained using only a few key protein sequence positions. These positions are stably selected by the forward feature selection based on the mutual information of factor-site position pairs.
authors.forename: G G
authors.initials: GG
authors.lastname: Fedonin
authors.forename: A B
authors.initials: AB
authors.lastname: Rakhmaninova
authors.forename: Iu D
authors.initials: IuD
authors.lastname: Korostelev
authors.forename: O N
authors.initials: ON
authors.lastname: Laĭkova
authors.forename: M S
authors.initials: MS
authors.lastname: Gel'fand
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D001499:Bayes Theorem; D001665:Binding Sites; D019295:Computational Biology; D004247:DNA / Q000737:chemistry / Q000378:metabolism*; D003627:Data Interpretation, Statistical; D030562:Databases, Protein; D056968:Lac Repressors / Q000737:chemistry / Q000378:metabolism*; D011485:Protein Binding; D016415:Sequence Alignment; D017422:Sequence Analysis, DNA / Q000379:methods* / Q000706:statistics & numerical data
chemical_list: D056968:Lac Repressors; D004247:DNA