Extracting sequence features to predict protein-DNA interactions: a comparative study.

ABSTRACT: Predicting how and where proteins, especially transcription factors (TFs), interact with DNA is an important problem in biology. We present here a systematic study of predictive modeling approaches to the TF-DNA binding problem, which have been frequently shown to be more efficient than those methods only based on position-specific weight matrices (PWMs). In these approaches, a statistical relationship between genomic sequences and gene expression or ChIP-binding intensities is inferred through a regression framework; and influential sequence features are identified by variable selection. We examine a few state-of-the-art learning methods including stepwise linear regression, multivariate adaptive regression splines, neural networks, support vector machines, boosting and Bayesian additive

SUBMITTER: Zhou Q 

PROVIDER: S-EPMC2475627 | biostudies-literature | 2008 Jul

REPOSITORIES: biostudies-literature