ArticleScientific reports2015
Improving Protein Fold Recognition by Deep Learning Networks.
Article in Scientific reports, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 31 papers.
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Who cites it
31 citing papers in PubMed.
- Protein Structure Prediction Methods.Advances in experimental medicine and biology · 2026Review
- The role of artificial intelligence in drug screening, drug design, and clinical trials.Frontiers in pharmacology · 2024Review
- BioSeq-Diabolo: Biological sequence similarity analysis using Diabolo.PLoS computational biology · 2023Article
- Protein Function Analysis through Machine Learning.Biomolecules · 2022Review
- Improving protein fold recognition using triplet network and ensemble deep learning.Briefings in bioinformatics · 2021Article
- FoldHSphere: deep hyperspherical embeddings for protein fold recognition.BMC bioinformatics · 2021Article
- Why can deep convolutional neural networks improve protein fold recognition? A visual explanation by interpretation.Briefings in bioinformatics · 2021Article
- Applications of artificial intelligence to drug design and discovery in the big data era: a comprehensive review.Molecular diversity · 2021Review
- Evolving scenario of big data and Artificial Intelligence (AI) in drug discovery.Molecular diversity · 2021Article
- A protein structural study based on the centrality analysis of protein sequence feature networks.PloS one · 2021Article
- Local Alignment of DNA Sequence Based on Deep Reinforcement Learning.IEEE open journal of engineering in medicine and biology · 2021Article
- ACEP: improving antimicrobial peptides recognition through automatic feature fusion and amino acid embedding.BMC genomics · 2020Article
- Network-based protein structural classification.Royal Society open science · 2020Article
- Article
- Deconvolution of autoencoders to learn biological regulatory modules from single cell mRNA sequencing data.BMC bioinformatics · 2019Article
- Employing a novel 2-gram subgroup intra pattern (2GSIP) with stacked auto encoder for membrane protein classification.Molecular biology reports · 2019Article
- Cell Identity Codes: Understanding Cell Identity from Gene Expression Profiles using Deep Neural Networks.Scientific reports · 2019Article
- A study on separation of the protein structural types in amino acid sequence feature spaces.PloS one · 2019Article
- Article
- Deep learning improves antimicrobial peptide recognition.Bioinformatics (Oxford, England) · 2018Article
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Authors and funding
4 authors.
Funding
Abstract
For accurate recognition of protein folds, a deep learning network method (DN-Fold) was developed to predict if a given query-template protein pair belongs to the same structural fold. The input used stemmed from the protein sequence and structural features extracted from the protein pair. We evaluated the performance of DN-Fold along with 18 different methods on Lindahl's benchmark dataset and on a large benchmark set extracted from SCOP 1.75 consisting of about one million protein pairs, at three different levels of fold recognition (i.e., protein family, superfamily, and fold) depending on the evolutionary distance between protein sequences. The correct recognition rate of ensembled DN-Fold for Top 1 predictions is 84.5%, 61.5%, and 33.6% and for Top 5 is 91.2%, 76.5%, and 60.7% at family, superfamily, and fold levels, respectively. We also evaluated the performance of single DN-Fold (DN-FoldS), which showed the comparable results at the level of family and superfamily, compared to ensemble DN-Fold. Finally, we extended the binary classification problem of fold recognition to real-value regression task, which also show a promising performance. DN-Fold is freely available through a web server at http://iris.rnet.missouri.edu/dnfold.
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