ArticleNucleic acids research2013
DNA motif elucidation using belief propagation.
Article in Nucleic acids research, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 27 papers, 1 of them a synthesis that pooled it.
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Who cites it
27 citing papers in PubMed, 1 synthesis or guideline pooled it, 69 citations in OpenAlex.
- Protein Science Meets Artificial Intelligence: A Systematic Review and a Biochemical Meta-Analysis of an Inter-Field.Frontiers in bioengineering and biotechnology · 2022Pooled it
- A DNABERT based deep learning framework for predicting transcription factor binding sites.Scientific reports · 2026Article
- UNet with Attention Networks: A Novel Deep Learning Approach for DNA Methylation Prediction in HeLa Cells.Genes · 2025Article
- WSHNN: A Weakly Supervised Hybrid Neural Network for the Identification of DNA-protein Binding Sites.Current computer-aided drug design · 2025Article
- Advancing epigenetic profiling in cervical cancer: machine learning techniques for classifying DNA methylation patterns.3 Biotech · 2024Article
- Comprehensive analysis of computational approaches in plant transcription factors binding regions discovery.Heliyon · 2024Article
- Discovering DNA shape motifs with multiple DNA shape features: generalization, methods, and validation.Nucleic acids research · 2024Article
- Predicting Transcription Factor Binding Sites with Deep Learning.International journal of molecular sciences · 2024Article
- Identification of DNA motif pairs on paired sequences based on composite heterogeneous graph.Frontiers in genetics · 2024Article
- RNANetMotif: Identifying sequence-structure RNA network motifs in RNA-protein binding sites.PLoS computational biology · 2022Article
- MAResNet: predicting transcription factor binding sites by combining multi-scale bottom-up and top-down attention and residual network.Briefings in bioinformatics · 2022Article
- SAResNet: self-attention residual network for predicting DNA-protein binding.Briefings in bioinformatics · 2021Article
- Computational Method for Classification of Avian Influenza A Virus Using DNA Sequence Information and Physicochemical Properties.Frontiers in genetics · 2021Article
- iProDNA-CapsNet: identifying protein-DNA binding residues using capsule neural networks.BMC bioinformatics · 2019Article
- Heterodimeric DNA motif synthesis and validations.Nucleic acids research · 2019Article
- Big data challenges in genome informatics.Biophysical reviews · 2019Article
- DNA Motif Recognition Modeling from Protein Sequences.iScience · 2018Article
- SamSelect: a sample sequence selection algorithm for quorum planted motif search on large DNA datasets.BMC bioinformatics · 2018Article
- A Novel Computational Method for Detecting DNA Methylation Sites with DNA Sequence Information and Physicochemical Properties.International journal of molecular sciences · 2018Article
- Regmex: a statistical tool for exploring motifs in ranked sequence lists from genomics experiments.Algorithms for molecular biology : AMB · 2018Article
Corrections and comments
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Authors and funding
5 authors at 3 institutions in 3 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Protein-binding microarray (PBM) is a high-throughout platform that can measure the DNA-binding preference of a protein in a comprehensive and unbiased manner. A typical PBM experiment can measure binding signal intensities of a protein to all the possible DNA k-mers (k=8∼10); such comprehensive binding affinity data usually need to be reduced and represented as motif models before they can be further analyzed and applied. Since proteins can often bind to DNA in multiple modes, one of the major challenges is to decompose the comprehensive affinity data into multimodal motif representations. Here, we describe a new algorithm that uses Hidden Markov Models (HMMs) and can derive precise and multimodal motifs using belief propagations. We describe an HMM-based approach using belief propagations (kmerHMM), which accepts and preprocesses PBM probe raw data into median-binding intensities of individual k-mers. The k-mers are ranked and aligned for training an HMM as the underlying motif representation. Multiple motifs are then extracted from the HMM using belief propagations. Comparisons of kmerHMM with other leading methods on several data sets demonstrated its effectiveness and uniqueness. Especially, it achieved the best performance on more than half of the data sets. In addition, the multiple binding modes derived by kmerHMM are biologically meaningful and will be useful in interpreting other genome-wide data such as those generated from ChIP-seq. The executables and source codes are available at the authors' websites: e.g. http://www.cs.toronto.edu/∼wkc/kmerHMM.
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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.