ArticleBioinformatics (Oxford, England)2017
Sequence2Vec: a novel embedding approach for modeling transcription factor binding affinity landscape.
Article in Bioinformatics (Oxford, England), 2017. 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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20 citing papers in PubMed.
- RGTBind: RBF-gate graph transformer with spatially biased attention for protein-DNA binding-site prediction.Journal of molecular modeling · 2026Article
- ntEmbd: Deep learning embedding for nucleotide sequences.bioRxiv : the preprint server for biology · 2024Article
- Deep learning in structural bioinformatics: current applications and future perspectives.Briefings in bioinformatics · 2024Review
- Analysis approaches for the identification and prediction ofEpigenetics · 2023Review
- Structural predictions of protein-DNA binding: MELD-DNA.Nucleic acids research · 2023Article
- EMDLP: Ensemble multiscale deep learning model for RNA methylation site prediction.BMC bioinformatics · 2022Article
- Machine learning: its challenges and opportunities in plant system biology.Applied microbiology and biotechnology · 2022Review
- Affinity2Vec: drug-target binding affinity prediction through representation learning, graph mining, and machine learning.Scientific reports · 2022Article
- Protein-RNA interaction prediction with deep learning: structure matters.Briefings in bioinformatics · 2022Article
- DTi2Vec: Drug-target interaction prediction using network embedding and ensemble learning.Journal of cheminformatics · 2021Article
- Attention-based multi-label neural networks for integrated prediction and interpretation of twelve widely occurring RNA modifications.Nature communications · 2021Article
- EDLmBMC bioinformatics · 2021Article
- Article
- Prediction of sgRNA on-target activity in bacteria by deep learning.BMC bioinformatics · 2019Article
- Gene2vec: gene subsequence embedding for prediction of mammalianRNA (New York, N.Y.) · 2019Article
- DNA Motif Recognition Modeling from Protein Sequences.iScience · 2018Article
- DeepSimulator: a deep simulator for Nanopore sequencing.Bioinformatics (Oxford, England) · 2018Article
- DLBI: deep learning guided Bayesian inference for structure reconstruction of super-resolution fluorescence microscopy.Bioinformatics (Oxford, England) · 2018Article
- DEEPre: sequence-based enzyme EC number prediction by deep learning.Bioinformatics (Oxford, England) · 2018Article
- mlDEEPre: Multi-Functional Enzyme Function Prediction With Hierarchical Multi-Label Deep Learning.Frontiers in genetics · 2018Article
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6 authors.
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Abstract
motivationAn accurate characterization of transcription factor (TF)-DNA affinity landscape is crucial to a quantitative understanding of the molecular mechanisms underpinning endogenous gene regulation. While recent advances in biotechnology have brought the opportunity for building binding affinity prediction methods, the accurate characterization of TF-DNA binding affinity landscape still remains a challenging problem.
resultsHere we propose a novel sequence embedding approach for modeling the transcription factor binding affinity landscape. Our method represents DNA binding sequences as a hidden Markov model which captures both position specific information and long-range dependency in the sequence. A cornerstone of our method is a novel message passing-like embedding algorithm, called Sequence2Vec, which maps these hidden Markov models into a common nonlinear feature space and uses these embedded features to build a predictive model. Our method is a novel combination of the strength of probabilistic graphical models, feature space embedding and deep learning. We conducted comprehensive experiments on over 90 large-scale TF-DNA datasets which were measured by different high-throughput experimental technologies. Sequence2Vec outperforms alternative machine learning methods as well as the state-of-the-art binding affinity prediction methods. AVAILABILITY AND IMPLEMENTATION: Our program is freely available at https://github.com/ramzan1990/sequence2vec. CONTACT: xin.gao@kaust.edu.sa or lsong@cc.gatech.edu. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
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