ArticleBioinformatics (Oxford, England)2010
Improved sequence-based prediction of disordered regions with multilayer fusion of multiple information sources.
Article in Bioinformatics (Oxford, England), 2010. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 78 papers, 1 of them a synthesis that pooled it.
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Who cites it
78 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Systematic analysis and prediction of type IV secreted effector proteins by machine learning approaches.Briefings in bioinformatics · 2019Pooled it
- Modern resources for intrinsic disorder predictions: protein language models, deep learning, meta-servers, and databases.Cellular and molecular life sciences : CMLS · 2026Review
- FusionEncoder: identification of intrinsically disordered regions based on multi-feature fusion.Bioinformatics (Oxford, England) · 2025Article
- A conserved motif in Henipavirus P/V/W proteins drives the fibrillation of the W protein from Hendra virus.Protein science : a publication of the Protein Society · 2025Article
- IDP-EDL: enhancing intrinsically disordered protein prediction by combining protein language model and ensemble deep learning.Briefings in bioinformatics · 2025Article
- Prediction of Intrinsically Disordered Lipid Binding Residues with DisoLipPred.Methods in molecular biology (Clifton, N.J.) · 2025Article
- Accurate Prediction of Protein-Binding Residues in Protein Sequences Using SCRIBER.Methods in molecular biology (Clifton, N.J.) · 2025Article
- Accurate and Fast Prediction of Intrinsic Disorder Using flDPnn.Methods in molecular biology (Clifton, N.J.) · 2025Article
- Recent Advances in Computational Prediction of Secondary and Supersecondary Structures from Protein Sequences.Methods in molecular biology (Clifton, N.J.) · 2025Review
- Protein intrinsically disordered region prediction by combining neural architecture search and multi-objective genetic algorithm.BMC biology · 2023Article
- Computational Prediction of Protein Intrinsically Disordered Region Related Interactions and Functions.Genes · 2023Review
- Computational prediction of disordered binding regions.Computational and structural biotechnology journal · 2023Review
- Compositional Bias of Intrinsically Disordered Proteins and Regions and Their Predictions.Biomolecules · 2022Article
- Roles of Physicochemical and Structural Properties of RNA-Binding Proteins in Predicting the Activities of Trans-Acting Splicing Factors with Machine Learning.International journal of molecular sciences · 2022Article
- Identifying Intrinsically Disordered Protein Regions through a Deep Neural Network with Three Novel Sequence Features.Life (Basel, Switzerland) · 2022Article
- Predicting Protein Conformational Disorder and Disordered Binding Sites.Methods in molecular biology (Clifton, N.J.) · 2022Article
- Prediction of protein-protein interaction sites in intrinsically disordered proteins.Frontiers in molecular biosciences · 2022Review
- Deep learning in prediction of intrinsic disorder in proteins.Computational and structural biotechnology journal · 2022Review
- Wheat inositol pyrophosphate kinase TaVIH2-3B modulates cell-wall composition and drought tolerance in Arabidopsis.BMC biology · 2021Article
- Polerovirus genomic variation.Virus evolution · 2021Article
18 more citing papers are in PubMed but not listed here.
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Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
motivationIntrinsically disordered proteins play a crucial role in numerous regulatory processes. Their abundance and ubiquity combined with a relatively low quantity of their annotations motivate research toward the development of computational models that predict disordered regions from protein sequences. Although the prediction quality of these methods continues to rise, novel and improved predictors are urgently needed.
resultsWe propose a novel method, named MFDp (Multilayered Fusion-based Disorder predictor), that aims to improve over the current disorder predictors. MFDp is as an ensemble of 3 Support Vector Machines specialized for the prediction of short, long and generic disordered regions. It combines three complementary disorder predictors, sequence, sequence profiles, predicted secondary structure, solvent accessibility, backbone dihedral torsion angles, residue flexibility and B-factors. Our method utilizes a custom-designed set of features that are based on raw predictions and aggregated raw values and recognizes various types of disorder. The MFDp is compared at the residue level on two datasets against eight recent disorder predictors and top-performing methods from the most recent CASP8 experiment. In spite of using training chains with <or=25% similarity to the test sequences, our method consistently and significantly outperforms the other methods based on the MCC index. The MFDp outperforms modern disorder predictors for the binary disorder assignment and provides competitive real-valued predictions. The MFDp's outputs are also shown to outperform the other methods in the identification of proteins with long disordered regions. AVAILABILITY: http://biomine.ece.ualberta.ca/MFDp.html.
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