ArticleProceedings. IEEE International Conference on Bioinformatics and Biomedicine2016
DeeperBind: Enhancing Prediction of Sequence Specificities of DNA Binding Proteins.
Article in Proceedings. IEEE International Conference on Bioinformatics and Biomedicine, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 50 papers.
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Who cites it
50 citing papers in PubMed, 154 citations in OpenAlex.
- Advances in Artificial Intelligence (AI) Models and Generative Algorithms Represent a New Paradigm for Genomics Research.International journal of molecular sciences · 2025Review
- RNA polymerase III transcription-associated polyadenylation promotes the accumulation of noncoding retrotransposons during infection.Proceedings of the National Academy of Sciences of the United States of America · 2025Article
- Identification and Characterization of the Complete Genome of the TGF-International journal of molecular sciences · 2025Article
- DeepMiRBP: a hybrid model for predicting microRNA-protein interactions based on transfer learning and cosine similarity.BMC bioinformatics · 2024Article
- The evaluation of transcription factor binding site prediction tools in human and Arabidopsis genomes.BMC bioinformatics · 2024Article
- DNA breathing integration with deep learning foundational model advances genome-wide binding prediction of human transcription factors.Nucleic acids research · 2024Article
- Identifying transcription factors with cell-type specific DNA binding signatures.BMC genomics · 2024Article
- EPI-Trans: an effective transformer-based deep learning model for enhancer promoter interaction prediction.BMC bioinformatics · 2024Article
- Predicting Transcription Factor Binding Sites with Deep Learning.International journal of molecular sciences · 2024Article
- Seq-RBPPred: Predicting RNA-Binding Proteins from Sequence.ACS omega · 2024Article
- Advancing Transcription Factor Binding Site Prediction Using DNA Breathing Dynamics and Sequence Transformers via Cross Attention.bioRxiv : the preprint server for biology · 2024Article
- Transfer Learning Allows Accurate RBP Target Site Prediction with Limited Sample Sizes.Biology · 2023Article
- A survey on algorithms to characterize transcription factor binding sites.Briefings in bioinformatics · 2023Review
- Applications of machine and deep learning to thyroid cytology and histopathology: a review.Frontiers in oncology · 2023Review
- NetTIME: a multitask and base-pair resolution framework for improved transcription factor binding site prediction.Bioinformatics (Oxford, England) · 2022Article
- DeepPN: a deep parallel neural network based on convolutional neural network and graph convolutional network for predicting RNA-protein binding sites.BMC bioinformatics · 2022Article
- Knowledge-guided artificial intelligence technologies for decoding complex multiomics interactions in cells.Clinical and experimental pediatrics · 2022Article
- Unsupervised Learning in Drug Design from Self-Organization to Deep Chemistry.International journal of molecular sciences · 2022Review
- Base-resolution prediction of transcription factor binding signals by a deep learning framework.PLoS computational biology · 2022Article
- Artificial Intelligence and Cardiovascular Genetics.Life (Basel, Switzerland) · 2022Review
Corrections and comments
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Authors and funding
2 authors at 1 institution in 1 country.
Funding
Abstract
Transcription factors (TFs) are macromolecules that bind to cis-regulatory specific sub-regions of DNA promoters and initiate transcription. Finding the exact location of these binding sites (aka motifs) is important in a variety of domains such as drug design and development. To address this need, several in vivo and in vitro techniques have been developed so far that try to characterize and predict the binding specificity of a protein to different DNA loci. The major problem with these techniques is that they are not accurate enough in prediction of the binding affinity and characterization of the corresponding motifs. As a result, downstream analysis is required to uncover the locations where proteins of interest bind. Here, we propose DeeperBind, a long short term recurrent convolutional network for prediction of protein binding specificities with respect to DNA probes. DeeperBind can model the positional dynamics of probe sequences and hence reckons with the contributions made by individual sub-regions in DNA sequences, in an effective way. Moreover, it can be trained and tested on datasets containing varying-length sequences. We apply our pipeline to the datasets derived from protein binding microarrays (PBMs), an in-vitro high-throughput technology for quantification of protein-DNA binding preferences, and present promising results. To the best of our knowledge, this is the most accurate pipeline that can predict binding specificities of DNA sequences from the data produced by high-throughput technologies through utilization of the power of deep learning for feature generation and positional dynamics modeling.
Identifiers
What OpenQuestion holds
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.