ArticleMolecular therapy. Nucleic acids2019
Identification of Intrinsically Disordered Proteins and Regions by Length-Dependent Predictors Based on Conditional Random Fields.
Article in Molecular therapy. Nucleic acids, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed.
- FusionEncoder: identification of intrinsically disordered regions based on multi-feature fusion.Bioinformatics (Oxford, England) · 2025Article
- IDP-EDL: enhancing intrinsically disordered protein prediction by combining protein language model and ensemble deep learning.Briefings in bioinformatics · 2025Article
- The structural landscape and diversity of Pyricularia oryzae MAX effectors revisited.PLoS pathogens · 2024Article
- Protein intrinsically disordered region prediction by combining neural architecture search and multi-objective genetic algorithm.BMC biology · 2023Article
- Deep learning in prediction of intrinsic disorder in proteins.Computational and structural biotechnology journal · 2022Review
- COMTOP: Protein Residue-Residue Contact Prediction through Mixed Integer Linear Optimization.Membranes · 2021Article
- RFPR-IDP: reduce the false positive rates for intrinsically disordered protein and region prediction by incorporating both fully ordered proteins and disordered proteins.Briefings in bioinformatics · 2021Article
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4 authors.
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Abstract
Accurate identification of intrinsically disordered proteins/regions (IDPs/IDRs) is critical for predicting protein structure and function. Previous studies have shown that IDRs of different lengths have different characteristics, and several classification-based predictors have been proposed for predicting different types of IDRs. Compared with these classification-based predictors, the previously proposed predictor IDP-CRF exhibits state-of-the-art performance for predicting IDPs/IDRs, which is a sequence labeling model based on conditional random fields (CRFs). Motivated by these methods, we propose a predictor called IDP-FSP, which is an ensemble of three CRF-based predictors called IDP-FSP-L, IDP-FSP-S, and IDP-FSP-G. These three predictors are specially designed to predict long, short, and generic disordered regions, respectively, and they are constructed based on different features. To the best of our knowledge, IDP-FSP is the first predictor that combines a sequence labeling algorithm with IDRs of different lengths. Experimental results using two independent test datasets show that IDP-FSP achieves better or at least comparable predictive performance with 26 existing state-of-the-art methods in this field, proving the effectiveness of IDP-FSP.
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