ArticleNucleic acids research2012
A flexible integrative approach based on random forest improves prediction of transcription factor binding sites.
Article in Nucleic acids research, 2012. 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, 57 citations in OpenAlex.
- A DNABERT based deep learning framework for predicting transcription factor binding sites.Scientific reports · 2026Article
- BERT-TFBS: a novel BERT-based model for predicting transcription factor binding sites by transfer learning.Briefings in bioinformatics · 2024Article
- Novel Grade Classification Tool with Lipidomics for Indica Rice Eating Quality Evaluation.Foods (Basel, Switzerland) · 2023Article
- Construction of a Diagnostic Model for Lymph Node Metastasis of the Papillary Thyroid Carcinoma Using Preoperative Ultrasound Features and Imaging Omics.Journal of healthcare engineering · 2022Article
- iT4SE-EP: Accurate Identification of Bacterial Type IV Secreted Effectors by Exploring Evolutionary Features from Two PSI-BLAST Profiles.Molecules (Basel, Switzerland) · 2021Article
- DeepGRN: prediction of transcription factor binding site across cell-types using attention-based deep neural networks.BMC bioinformatics · 2021Article
- Machine Learning for Integrating Data in Biology and Medicine: Principles, Practice, and Opportunities.An international journal on information fusion · 2019Article
- Mapping specificity landscapes of RNA-protein interactions by high throughput sequencing.Methods (San Diego, Calif.) · 2017Article
- Quantitative modeling of gene expression using DNA shape features of binding sites.Nucleic acids research · 2016Article
- A DNA shape-based regulatory score improves position-weight matrix-based recognition of transcription factor binding sites.Bioinformatics (Oxford, England) · 2015Article
- Specificity and nonspecificity in RNA-protein interactions.Nature reviews. Molecular cell biology · 2015Review
- Contribution of Sequence Motif, Chromatin State, and DNA Structure Features to Predictive Models of Transcription Factor Binding in Yeast.PLoS computational biology · 2015Article
- Article
- GBshape: a genome browser database for DNA shape annotations.Nucleic acids research · 2015Article
- Genome-wide analysis of transcription factor binding sites and their characteristic DNA structures.BMC genomics · 2015Article
- 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
- TFBSshape: a motif database for DNA shape features of transcription factor binding sites.Nucleic acids research · 2014Article
- PhysBinder: Improving the prediction of transcription factor binding sites by flexible inclusion of biophysical properties.Nucleic acids research · 2013Article
- Binding of nucleoid-associated protein fis to DNA is regulated by DNA breathing dynamics.PLoS computational biology · 2013Article
Corrections and comments
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Authors and funding
4 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Transcription factor binding sites (TFBSs) are DNA sequences of 6-15 base pairs. Interaction of these TFBSs with transcription factors (TFs) is largely responsible for most spatiotemporal gene expression patterns. Here, we evaluate to what extent sequence-based prediction of TFBSs can be improved by taking into account the positional dependencies of nucleotides (NPDs) and the nucleotide sequence-dependent structure of DNA. We make use of the random forest algorithm to flexibly exploit both types of information. Results in this study show that both the structural method and the NPD method can be valuable for the prediction of TFBSs. Moreover, their predictive values seem to be complementary, even to the widely used position weight matrix (PWM) method. This led us to combine all three methods. Results obtained for five eukaryotic TFs with different DNA-binding domains show that our method improves classification accuracy for all five eukaryotic TFs compared with other approaches. Additionally, we contrast the results of seven smaller prokaryotic sets with high-quality data and show that with the use of high-quality data we can significantly improve prediction performance. Models developed in this study can be of great use for gaining insight into the mechanisms of TF binding.
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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.