ArticleBMC bioinformatics2005
Scoring functions for transcription factor binding site prediction.
Article in BMC bioinformatics, 2005. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed.
- An improved systematic approach to predicting transcription factor target genes using support vector machine.PloS one · 2014Article
- Modeling regulatory cascades using Artificial Neural Networks: the case of transcriptional regulatory networks shaped during the yeast stress response.Frontiers in genetics · 2013Article
- Inclusion of neighboring base interdependencies substantially improves genome-wide prokaryotic transcription factor binding site prediction.Nucleic acids research · 2010Article
- Binding site graphs: a new graph theoretical framework for prediction of transcription factor binding sites.PLoS computational biology · 2007Article
- Discovering motifs in ranked lists of DNA sequences.PLoS computational biology · 2007Article
- Positional clustering improves computational binding site detection and identifies novel cis-regulatory sites in mammalian GABAA receptor subunit genes.Nucleic acids research · 2007Article
- EXMOTIF: efficient structured motif extraction.Algorithms for molecular biology : AMB · 2006Article
- GibbsST: a Gibbs sampling method for motif discovery with enhanced resistance to local optima.BMC bioinformatics · 2006Article
- XcisClique: analysis of regulatory bicliques.BMC bioinformatics · 2006Article
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundTranscription factor binding site (TFBS) prediction is a difficult problem, which requires a good scoring function to discriminate between real binding sites and background noise. Many scoring functions have been proposed in the literature, but it is difficult to assess their relative performance, because they are implemented in different software tools using different search methods and different TFBS representations.
resultsHere we compare how several scoring functions perform on both real and semi-simulated data sets in a common test environment. We have also developed two new scoring functions and included them in the comparison. The data sets are from the yeast (S. cerevisiae) genome. Our new scoring function LLBG (least likely under the background model) performs best in this study. It achieves the best average rank for the correct motifs. Scoring functions based on positional bias performed quite poorly in this study.
conclusionLLBG may provide an interesting alternative to current scoring functions for TFBS prediction.
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