ArticleScientific reports2018
Improving Protein Gamma-Turn Prediction Using Inception Capsule Networks.
Article in Scientific reports, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed.
- Review
- Recent Advances in Computational Prediction of Secondary and Supersecondary Structures from Protein Sequences.Methods in molecular biology (Clifton, N.J.) · 2025Review
- Evaluation of the Effectiveness of Derived Features of AlphaFold2 on Single-Sequence Protein Binding Site Prediction.Biology · 2022Article
- Active disease-related compound identification based on capsule network.Briefings in bioinformatics · 2022Article
- Quantitative Assessment of Chirality of Protein Secondary Structures and Phenylalanine Peptide Nanotubes.Nanomaterials (Basel, Switzerland) · 2021Article
- MultiCapsNet: A General Framework for Data Integration and Interpretable Classification.Frontiers in genetics · 2021Article
- TMP- SSurface2: A Novel Deep Learning-Based Surface Accessibility Predictor for Transmembrane Protein Sequence.Frontiers in genetics · 2021Article
- MUFold-SSW: a new web server for predicting protein secondary structures, torsion angles and turns.Bioinformatics (Oxford, England) · 2020Article
- TMPSS: A Deep Learning-Based Predictor for Secondary Structure and Topology Structure Prediction of Alpha-Helical Transmembrane Proteins.Frontiers in bioengineering and biotechnology · 2020Article
Corrections and comments
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Authors and funding
3 authors.
Funding
Abstract
Protein gamma-turn prediction is useful in protein function studies and experimental design. Several methods for gamma-turn prediction have been developed, but the results were unsatisfactory with Matthew correlation coefficients (MCC) around 0.2-0.4. Hence, it is worthwhile exploring new methods for the prediction. A cutting-edge deep neural network, named Capsule Network (CapsuleNet), provides a new opportunity for gamma-turn prediction. Even when the number of input samples is relatively small, the capsules from CapsuleNet are effective to extract high-level features for classification tasks. Here, we propose a deep inception capsule network for gamma-turn prediction. Its performance on the gamma-turn benchmark GT320 achieved an MCC of 0.45, which significantly outperformed the previous best method with an MCC of 0.38. This is the first gamma-turn prediction method utilizing deep neural networks. Also, to our knowledge, it is the first published bioinformatics application utilizing capsule network, which will provide a useful example for the community. Executable and source code can be download at http://dslsrv8.cs.missouri.edu/~cf797/MUFoldGammaTurn/download.html.
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Registered trials
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