ArticlePloS one2013
Hierarchical classification of protein folds using a novel ensemble classifier.
Article in PloS one, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 58 papers.
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Who cites it
58 citing papers in PubMed.
- Trial
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- Ensemble learning-based feature selection for phosphorylation site detection.Frontiers in genetics · 2022Article
- iKcr_CNN: A novel computational tool for imbalance classification of human nonhistone crotonylation sites based on convolutional neural networks with focal loss.Computational and structural biotechnology journal · 2022Article
- Predicting subcellular location of protein with evolution information and sequence-based deep learning.BMC bioinformatics · 2021Article
- HumDLoc: Human Protein Subcellular Localization Prediction Using Deep Neural Network.Current genomics · 2020Article
- Network-based protein structural classification.Royal Society open science · 2020Article
- DeepRTCP: Predicting ATP-Binding Cassette Transporters Based on 1-Dimensional Convolutional Network.Frontiers in cell and developmental biology · 2020Article
- A study on separation of the protein structural types in amino acid sequence feature spaces.PloS one · 2019Article
- An Ensemble Strategy to Predict Prognosis in Ovarian Cancer Based on Gene Modules.Frontiers in genetics · 2019Article
- A New Method for Recognizing Cytokines Based on Feature Combination and a Support Vector Machine Classifier.Molecules (Basel, Switzerland) · 2018Article
- Deep Neural Network Based Predictions of Protein Interactions Using Primary Sequences.Molecules (Basel, Switzerland) · 2018Article
- Article
- RFAmyloid: A Web Server for Predicting Amyloid Proteins.International journal of molecular sciences · 2018Article
- Ensemble Consensus-Guided Unsupervised Feature Selection to Identify Huntington's Disease-Associated Genes.Genes · 2018Article
- Identifying Plant Pentatricopeptide Repeat Coding Gene/Protein Using Mixed Feature Extraction Methods.Frontiers in plant science · 2018Article
- PredT4SE-Stack: Prediction of Bacterial Type IV Secreted Effectors From Protein Sequences Using a Stacked Ensemble Method.Frontiers in microbiology · 2018Article
- Identification of DNA-protein Binding Sites through Multi-Scale Local Average Blocks on Sequence Information.Molecules (Basel, Switzerland) · 2017Article
- Predicting Protein Submitochondrial Locations: The 10th Anniversary.Current genomics · 2017Review
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Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The analysis of biological information from protein sequences is important for the study of cellular functions and interactions, and protein fold recognition plays a key role in the prediction of protein structures. Unfortunately, the prediction of protein fold patterns is challenging due to the existence of compound protein structures. Here, we processed the latest release of the Structural Classification of Proteins (SCOP, version 1.75) database and exploited novel techniques to impressively increase the accuracy of protein fold classification. The techniques proposed in this paper include ensemble classifying and a hierarchical framework, in the first layer of which similar or redundant sequences were deleted in two manners; a set of base classifiers, fused by various selection strategies, divides the input into seven classes; in the second layer of which, an analogous ensemble method is adopted to predict all protein folds. To our knowledge, it is the first time all protein folds can be intelligently detected hierarchically. Compared with prior studies, our experimental results demonstrated the efficiency and effectiveness of our proposed method, which achieved a success rate of 74.21%, which is much higher than results obtained with previous methods (ranging from 45.6% to 70.5%). When applied to the second layer of classification, the prediction accuracy was in the range between 23.13% and 46.05%. This value, which may not be remarkably high, is scientifically admirable and encouraging as compared to the relatively low counts of proteins from most fold recognition programs. The web server Hierarchical Protein Fold Prediction (HPFP) is available at http://datamining.xmu.edu.cn/software/hpfp.
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