ArticlePloS one2011
A linear model for transcription factor binding affinity prediction in protein binding microarrays.
Article in PloS one, 2011. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 23 papers.
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Who cites it
23 citing papers in PubMed.
- Evaluating a linear k-mer model for protein-DNA interactions using high-throughput SELEX data.BMC bioinformatics · 2013Trial
- Navigating the pitfalls of applying machine learning in genomics.Nature reviews. Genetics · 2022Review
- Weakly supervised learning of RNA modifications from low-resolution epitranscriptome data.Bioinformatics (Oxford, England) · 2021Article
- TFBSshape: an expanded motif database for DNA shape features of transcription factor binding sites.Nucleic acids research · 2020Article
- Modeling in-vivo protein-DNA binding by combining multiple-instance learning with a hybrid deep neural network.Scientific reports · 2019Article
- Divergence in DNA Specificity among Paralogous Transcription Factors Contributes to Their Differential In Vivo Binding.Cell systems · 2018Article
- Comprehensive, high-resolution binding energy landscapes reveal context dependencies of transcription factor binding.Proceedings of the National Academy of Sciences of the United States of America · 2018Article
- Sequence2Vec: a novel embedding approach for modeling transcription factor binding affinity landscape.Bioinformatics (Oxford, England) · 2017Article
- Computational modeling of in vivo and in vitro protein-DNA interactions by multiple instance learning.Bioinformatics (Oxford, England) · 2017Article
- Quantifying the Impact of Non-coding Variants on Transcription Factor-DNA Binding.Research in computational molecular biology : ... Annual International Conference, RECOMB ... : proceedings. RECOMB (Conference : 2005- ) · 2017Article
- Modeling protein-DNA binding via high-throughput in vitro technologies.Briefings in functional genomics · 2017Article
- Article
- Crowdsourcing biomedical research: leveraging communities as innovation engines.Nature reviews. Genetics · 2016Review
- The jigsaw puzzle of sequence phenotype inference: Piecing together Shannon entropy, importance sampling, and Empirical Bayes.Journal of theoretical biology · 2015Article
- MARZ: an algorithm to combinatorially analyze gapped n-mer models of transcription factor binding.BMC bioinformatics · 2015Article
- Transcription factor motif quality assessment requires systematic comparative analysis.F1000Research · 2015Article
- MORPHEUS, a Webtool for Transcription Factor Binding Analysis Using Position Weight Matrices with Dependency.PloS one · 2015Article
- Absence of a simple code: how transcription factors read the genome.Trends in biochemical sciences · 2014Review
- Modeling DNA affinity landscape through two-round support vector regression with weighted degree kernels.BMC systems biology · 2014Article
- Predicting tissue specific transcription factor binding sites.BMC genomics · 2013Article
Corrections and comments
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Protein binding microarrays (PBM) are a high throughput technology used to characterize protein-DNA binding. The arrays measure a protein's affinity toward thousands of double-stranded DNA sequences at once, producing a comprehensive binding specificity catalog. We present a linear model for predicting the binding affinity of a protein toward DNA sequences based on PBM data. Our model represents the measured intensity of an individual probe as a sum of the binding affinity contributions of the probe's subsequences. These subsequences characterize a DNA binding motif and can be used to predict the intensity of protein binding against arbitrary DNA sequences. Our method was the best performer in the Dialogue for Reverse Engineering Assessments and Methods 5 (DREAM5) transcription factor/DNA motif recognition challenge. For the DREAM5 bonus challenge, we also developed an approach for the identification of transcription factors based on their PBM binding profiles. Our approach for TF identification achieved the best performance in the bonus challenge.
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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.