title: An Entropy-Based Position Projection Algorithm for Motif Discovery.
abstract: Motif discovery problem is crucial for understanding the structure and function of gene expression. Over the past decades, many attempts using consensus and probability training model for motif finding are successful. However, the most existing motif discovery algorithms are still time-consuming or easily trapped in a local optimum. To overcome these shortcomings, in this paper, we propose an entropy-based position projection algorithm, called EPP, which designs a projection process to divide the dataset and explores the best local optimal solution. The experimental results on real DNA sequences, Tompa data, and ChIP-seq data show that EPP is advantageous in dealing with the motif discovery problem and outperforms current widely used algorithms.
authors.affiliation: Department of Automation, School of Electronics and Control Engineering, Chang'An University, Xi'an 710064, China.
authors.forename: Yipu
authors.identifier: 0000-0001-5669-5292
authors.initials: Y
authors.lastname: Zhang
authors.affiliation: Department of Automation, School of Electronics and Control Engineering, Chang'An University, Xi'an 710064, China.
authors.forename: Ping
authors.initials: P
authors.lastname: Wang
authors.affiliation: Department of Automation, School of Electronics and Control Engineering, Chang'An University, Xi'an 710064, China.
authors.forename: Maode
authors.initials: M
authors.lastname: Yan
mesh_terms: D000465:Algorithms; D000818:Animals; D030561:Databases, Nucleic Acid; D019277:Entropy; D051379:Mice; D000066450:Mouse Embryonic Stem Cells / Q000378:metabolism; D059372:Nucleotide Motifs / Q000235:genetics*; D056510:Position-Specific Scoring Matrices; D012984:Software; D013997:Time Factors