ArticleProceedings of ... International Joint Conference on Neural Networks. International Joint Conference on Neural Networks2014
DL-PRO: A Novel Deep Learning Method for Protein Model Quality Assessment.
Article in Proceedings of ... International Joint Conference on Neural Networks. International Joint Conference on Neural Networks, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers.
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Who cites it
18 citing papers in PubMed.
- A Multitask Active Learning Framework with Probabilistic Modeling for Multi-Species Acute Toxicity Prediction.Molecules (Basel, Switzerland) · 2026Article
- Deep learning in structural bioinformatics: current applications and future perspectives.Briefings in bioinformatics · 2024Review
- Deep Learning for Genomics: From Early Neural Nets to Modern Large Language Models.International journal of molecular sciences · 2023Review
- Protein Structure Prediction: Challenges, Advances, and the Shift of Research Paradigms.Genomics, proteomics & bioinformatics · 2023Review
- Illuminating the "Twilight Zone": Advances in Difficult Protein Modeling.Methods in molecular biology (Clifton, N.J.) · 2023Article
- Research on classification method of high myopic maculopathy based on retinal fundus images and optimized ALFA-Mix active learning algorithm.International journal of ophthalmology · 2023Article
- Decoy selection for protein structure prediction via extreme gradient boosting and ranking.BMC bioinformatics · 2020Article
- From Knowledge Transmission to Knowledge Construction: A Step towards Human-Like Active Learning.Entropy (Basel, Switzerland) · 2020Article
- Machine Learning Approaches for Quality Assessment of Protein Structures.Biomolecules · 2020Review
- Unsupervised and Supervised Learning over theEnergy Landscape for Protein Decoy Selection.Biomolecules · 2019Article
- Reverse active learning based atrous DenseNet for pathological image classification.BMC bioinformatics · 2019Article
- Graph-Based Community Detection for Decoy Selection in Template-Free Protein Structure Prediction.Molecules (Basel, Switzerland) · 2019Article
- EnzyNet: enzyme classification using 3D convolutional neural networks on spatial representation.PeerJ · 2018Article
- iSS-PC: Identifying Splicing Sites via Physical-Chemical Properties Using Deep Sparse Auto-Encoder.Scientific reports · 2017Article
- Sorting protein decoys by machine-learning-to-rank.Scientific reports · 2016Article
- Deep Artificial Neural Networks and Neuromorphic Chips for Big Data Analysis: Pharmaceutical and Bioinformatics Applications.International journal of molecular sciences · 2016Review
- Benchmarking Deep Networks for Predicting Residue-Specific Quality of Individual Protein Models in CASP11.Scientific reports · 2016Article
- New Deep Learning Methods for Protein Loop Modeling.IEEE/ACM transactions on computational biology and bioinformaticsArticle
Corrections and comments
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Authors and funding
3 authors.
Funding
Abstract
Computational protein structure prediction is very important for many applications in bioinformatics. In the process of predicting protein structures, it is essential to accurately assess the quality of generated models. Although many single-model quality assessment (QA) methods have been developed, their accuracy is not high enough for most real applications. In this paper, a new approach based on C-α atoms distance matrix and machine learning methods is proposed for single-model QA and the identification of native-like models. Different from existing energy/scoring functions and consensus approaches, this new approach is purely geometry based. Furthermore, a novel algorithm based on deep learning techniques, called DL-Pro, is proposed. For a protein model, DL-Pro uses its distance matrix that contains pairwise distances between two residues' C-α atoms in the model, which sometimes is also called contact map, as an orientation-independent representation. From training examples of distance matrices corresponding to good and bad models, DL-Pro learns a stacked autoencoder network as a classifier. In experiments on selected targets from the Critical Assessment of Structure Prediction (CASP) competition, DL-Pro obtained promising results, outperforming state-of-the-art energy/scoring functions, including OPUS-CA, DOPE, DFIRE, and RW.
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Registered trials
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