ArticleBioinformatics (Oxford, England)2012
Predicting protein residue-residue contacts using deep networks and boosting.
Article in Bioinformatics (Oxford, England), 2012. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 72 papers.
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Who cites it
72 citing papers in PubMed.
- Integrating protein and DNA embeddings for improving genome-wide transcription factor binding site prediction.NAR genomics and bioinformatics · 2026Article
- Artificial intelligence driven protein design and sustainable nanomedicine for advanced theranostics.Bioactive materials · 2026Review
- Peptide-protein docking: from physics-based models to generative intelligence.Chemical communications (Cambridge, England) · 2026Review
- AI-Driven Protein Research: From Prediction to Design.Methods in molecular biology (Clifton, N.J.) · 2026Review
- Boosting AlphaFold protein tertiary structure prediction through MSA engineering and extensive model sampling and ranking in CASP16.Communications biology · 2025Article
- MPBind: a multitask protein binding site predictor using protein language models and equivariant GNNs.Bioinformatics (Oxford, England) · 2025Article
- Chemosensory Receptors in Vertebrates: Structure and Computational Modeling Insights.International journal of molecular sciences · 2025Review
- Importance of Inter-residue Contacts for Understanding Protein Folding and Unfolding Rates, Remote Homology, and Drug Design.Molecular biotechnology · 2025Review
- Diffusion models in bioinformatics and computational biology.Nature reviews bioengineering · 2024Article
- Article
- Combining pairwise structural similarity and deep learning interface contact prediction to estimate protein complex model accuracy in CASP15.bioRxiv : the preprint server for biology · 2023Article
- Illuminating the "Twilight Zone": Advances in Difficult Protein Modeling.Methods in molecular biology (Clifton, N.J.) · 2023Article
- Prediction of inter-chain distance maps of protein complexes with 2D attention-based deep neural networks.Nature communications · 2022Article
- Deep learning methods for 3D structural proteome and interactome modeling.Current opinion in structural biology · 2022Review
- A tale of solving two computational challenges in protein science: neoantigen prediction and protein structure prediction.Briefings in bioinformatics · 2022Review
- Comparative Analysis on Alignment-Based and Pretrained Feature Representations for the Identification of DNA-Binding Proteins.Computational and mathematical methods in medicine · 2022Article
- Machine-Learned Molecular Surface and Its Application to Implicit Solvent Simulations.Journal of chemical theory and computation · 2021Article
- Improved protein structure prediction by deep learning irrespective of co-evolution information.Nature machine intelligence · 2021Article
- Toward the solution of the protein structure prediction problem.The Journal of biological chemistry · 2021Review
- Combination of deep neural network with attention mechanism enhances the explainability of protein contact prediction.Proteins · 2021Article
12 more citing papers are in PubMed but not listed here.
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Authors and funding
2 authors.
Funding
Abstract
motivationProtein residue-residue contacts continue to play a larger and larger role in protein tertiary structure modeling and evaluation. Yet, while the importance of contact information increases, the performance of sequence-based contact predictors has improved slowly. New approaches and methods are needed to spur further development and progress in the field.
resultsHere we present DNCON, a new sequence-based residue-residue contact predictor using deep networks and boosting techniques. Making use of graphical processing units and CUDA parallel computing technology, we are able to train large boosted ensembles of residue-residue contact predictors achieving state-of-the-art performance. AVAILABILITY: The web server of the prediction method (DNCON) is available at http://iris.rnet.missouri.edu/dncon/. CONTACT: chengji@missouri.edu SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
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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.