ArticleInternational journal of molecular sciences2018
IDP⁻CRF: Intrinsically Disordered Protein/Region Identification Based on Conditional Random Fields.
Article in International journal of molecular sciences, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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Who cites it
11 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
- FusOn-pLM: a fusion oncoprotein-specific language model via adjusted rate masking.Nature communications · 2025Article
- Special Protein or RNA Molecules Computational Identification.International journal of molecular sciences · 2023Article
- Deep learning in prediction of intrinsic disorder in proteins.Computational and structural biotechnology journal · 2022Review
- BioSeq-BLM: a platform for analyzing DNA, RNA and protein sequences based on biological language models.Nucleic acids research · 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
- A deep learning model for plant lncRNA-protein interaction prediction with graph attention.Molecular genetics and genomics : MGG · 2020Article
- BioSeq-Analysis2.0: an updated platform for analyzing DNA, RNA and protein sequences at sequence level and residue level based on machine learning approaches.Nucleic acids research · 2019Article
- Identification of Intrinsically Disordered Proteins and Regions by Length-Dependent Predictors Based on Conditional Random Fields.Molecular therapy. Nucleic acids · 2019Article
- Gene2vec: gene subsequence embedding for prediction of mammalianRNA (New York, N.Y.) · 2019Article
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3 authors.
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Abstract
Accurate prediction of intrinsically disordered proteins/regions is one of the most important tasks in bioinformatics, and some computational predictors have been proposed to solve this problem. How to efficiently incorporate the sequence-order effect is critical for constructing an accurate predictor because disordered region distributions show global sequence patterns. In order to capture these sequence patterns, several sequence labelling models have been applied to this field, such as conditional random fields (CRFs). However, these methods suffer from certain disadvantages. In this study, we proposed a new computational predictor called IDP⁻CRF, which is trained on an updated benchmark dataset based on the MobiDB database and the DisProt database, and incorporates more comprehensive sequence-based features, including PSSMs (position-specific scoring matrices), kmer, predicted secondary structures, and relative solvent accessibilities. Experimental results on the benchmark dataset and two independent datasets show that IDP⁻CRF outperforms 25 existing state-of-the-art methods in this field, demonstrating that IDP⁻CRF is a very useful tool for identifying IDPs/IDRs (intrinsically disordered proteins/regions). We anticipate that IDP⁻CRF will facilitate the development of protein sequence analysis.
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