Occupancy classification of position weight matrix-inferred transcription factor binding sites.

ABSTRACT:

Background

Computational prediction of Transcription Factor Binding Sites (TFBS) from sequence data alone is difficult and error-prone. Machine learning techniques utilizing additional environmental information about a predicted binding site (such as distances from the site to particular chromatin features) to determine its occupancy/functionality class show promise as methods to achieve more accurate prediction of true TFBS in silico. We evaluate the Bayesian Network (BN) and Support Vector Machine (SVM) machine learning techniques on four distinct TFBS data sets and analyze their performance. We describe the features that are most useful for classification and contrast and compare these feature sets between the factors.

Results

Our results demonstrate good performance of c

SUBMITTER: Wright H 

PROVIDER: S-EPMC3208542 | biostudies-literature | 2011

REPOSITORIES: biostudies-literature