ArticleInternational journal of molecular sciences2015
DeepCNF-D: Predicting Protein Order/Disorder Regions by Weighted Deep Convolutional Neural Fields.
Article in International journal of molecular sciences, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 32 papers.
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Who cites it
32 citing papers in PubMed, 73 citations in OpenAlex.
- Impact of intrinsically disordered regions and functional disorder hotspots in the human kinome.Briefings in bioinformatics · 2025Article
- A genetic algorithm-based ensemble model for efficiently identifying interleukin 6 inducing peptides.Scientific reports · 2025Article
- PredIL13: Stacking a variety of machine and deep learning methods with ESM-2 language model for identifying IL13-inducing peptides.PloS one · 2024Article
- ADOPT: intrinsic protein disorder prediction through deep bidirectional transformers.NAR genomics and bioinformatics · 2023Article
- Computational Prediction of Protein Intrinsically Disordered Region Related Interactions and Functions.Genes · 2023Review
- Estimation of model accuracy by a unique set of features and tree-based regressor.Scientific reports · 2022Article
- An Interpretable Double-Scale Attention Model for Enzyme Protein Class Prediction Based on Transformer Encoders and Multi-Scale Convolutions.Frontiers in genetics · 2022Article
- Deep learning in prediction of intrinsic disorder in proteins.Computational and structural biotechnology journal · 2022Review
- Explainable deep neural networks for novel viral genome prediction.Applied intelligence (Dordrecht, Netherlands) · 2022Article
- Diverse Scientific Benchmarks for Implicit Membrane Energy Functions.Journal of chemical theory and computation · 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
- Comparative Assessment of Intrinsic Disorder Predictions with a Focus on Protein and Nucleic Acid-Binding Proteins.Biomolecules · 2020Review
- Predicting the Real-Valued Inter-Residue Distances for Proteins.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2020Article
- RDbBMC bioinformatics · 2020Article
- Evaluation of deep and shallow learning methods in chemogenomics for the prediction of drugs specificity.Journal of cheminformatics · 2020Article
- The Order-Disorder Continuum: Linking Predictions of Protein Structure and Disorder through Molecular Simulation.Scientific reports · 2020Review
- Prediction of secondary structure population and intrinsic disorder of proteins using multitask deep learning.AMIA ... Annual Symposium proceedings. AMIA Symposium · 2020Article
- DISOselect: Disorder predictor selection at the protein level.Protein science : a publication of the Protein Society · 2020Article
- Article
- PredMP: a web server for de novo prediction and visualization of membrane proteins.Bioinformatics (Oxford, England) · 2019Article
Corrections and comments
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Authors and funding
4 authors at 3 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Intrinsically disordered proteins or protein regions are involved in key biological processes including regulation of transcription, signal transduction, and alternative splicing. Accurately predicting order/disorder regions ab initio from the protein sequence is a prerequisite step for further analysis of functions and mechanisms for these disordered regions. This work presents a learning method, weighted DeepCNF (Deep Convolutional Neural Fields), to improve the accuracy of order/disorder prediction by exploiting the long-range sequential information and the interdependency between adjacent order/disorder labels and by assigning different weights for each label during training and prediction to solve the label imbalance issue. Evaluated by the CASP9 and CASP10 targets, our method obtains 0.855 and 0.898 AUC values, which are higher than the state-of-the-art single ab initio predictors.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.