ArticleNucleic acids research2008
Extracting sequence features to predict protein-DNA interactions: a comparative study.
Article in Nucleic acids research, 2008. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers.
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Who cites it
20 citing papers in PubMed, 2 citations in OpenAlex.
- Article
- An Efficient and Effective Model to Handle Missing Data in Classification.BioMed research international · 2020Article
- Mapping specificity landscapes of RNA-protein interactions by high throughput sequencing.Methods (San Diego, Calif.) · 2017Article
- Specificity and nonspecificity in RNA-protein interactions.Nature reviews. Molecular cell biology · 2015Review
- Predicting chromatin organization using histone marks.Genome biology · 2015Article
- An overview of the prediction of protein DNA-binding sites.International journal of molecular sciences · 2015Review
- Protein-DNA binding in high-resolution.Critical reviews in biochemistry and molecular biology · 2015Review
- Absence of a simple code: how transcription factors read the genome.Trends in biochemical sciences · 2014Review
- Applications of alignment-free methods in epigenomics.Briefings in bioinformatics · 2014Review
- DNA motif elucidation using belief propagation.Nucleic acids research · 2013Article
- Genome-wide computational prediction and analysis of core promoter elements across plant monocots and dicots.PloS one · 2013Article
- Understanding variation in transcription factor binding by modeling transcription factor genome-epigenome interactions.PLoS computational biology · 2013Article
- Searching ChIP-seq genomic islands for combinatorial regulatory codes in mouse embryonic stem cells.BMC genomics · 2011Article
- Discovering protein-DNA binding sequence patterns using association rule mining.Nucleic acids research · 2010Article
- Statistics of protein-DNA binding and the total number of binding sites for a transcription factor in the mammalian genome.BMC genomics · 2010Article
- ChIP-Seq of transcription factors predicts absolute and differential gene expression in embryonic stem cells.Proceedings of the National Academy of Sciences of the United States of America · 2009Article
- A biophysical model for analysis of transcription factor interaction and binding site arrangement from genome-wide binding data.PloS one · 2009Article
- BayesPI - a new model to study protein-DNA interactions: a case study of condition-specific protein binding parameters for Yeast transcription factors.BMC bioinformatics · 2009Article
- Exhaustive search for over-represented DNA sequence motifs with CisFinder.DNA research : an international journal for rapid publication of reports on genes and genomes · 2009Article
- Linking genome to epigenome.Wiley interdisciplinary reviews. Systems biology and medicineReview
Corrections and comments
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Authors and funding
2 authors at 1 institution in 1 country.
Funding
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 regression trees (BART). These methods are applied to both simulated datasets and two whole-genome ChIP-chip datasets on the TFs Oct4 and Sox2, respectively, in human embryonic stem cells. We find that, with proper learning methods, predictive modeling approaches can significantly improve the predictive power and identify more biologically interesting features, such as TF-TF interactions, than the PWM approach. In particular, BART and boosting show the best and the most robust overall performance among all the methods.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.