Machine learning study of DNA binding by transcription factors from the LacI family

G. G. Fedonin, A. B. Rakhmaninova, Yu D. Korostelev, O. N. Laikova, M. S. Gelfand

Research output: Contribution to journalArticlepeer-review

1 Citation (Scopus)

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.

Original languageEnglish
Pages (from-to)667-679
Number of pages13
JournalMolecular Biology
Volume45
Issue number4
DOIs
Publication statusPublished - Aug 2011
Externally publishedYes

Keywords

  • LacI family
  • Logistic Regression
  • Mutual Information
  • Naive Bayes classifier
  • prokaryotes
  • transcription factors

Fingerprint

Dive into the research topics of 'Machine learning study of DNA binding by transcription factors from the LacI family'. Together they form a unique fingerprint.

Cite this