ArticlePloS one2021
OCLSTM: Optimized convolutional and long short-term memory neural network model for protein secondary structure prediction.
Article in PloS one, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers, 1 of them a synthesis that pooled it.
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Who cites it
10 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Advances in the Application of Protein Language Modeling for Nucleic Acid Protein Binding Site Prediction.Genes · 2024Pooled it
- Examine neural network models for protein sequencing based features prediction.Journal of computer-aided molecular design · 2026Article
- Deep learning based prediction of RNA 5hmC modifications using composite feature representations and comparative benchmarking with transformer models.BioData mining · 2026Article
- Improving Protein Secondary Structure Prediction by Deep Language Models and Transformer Networks.Methods in molecular biology (Clifton, N.J.) · 2025Article
- Machine Learning Techniques to Infer Protein Structure and Function from Sequences: A Comprehensive Review.Methods in molecular biology (Clifton, N.J.) · 2025Review
- Recent Advances in Computational Prediction of Secondary and Supersecondary Structures from Protein Sequences.Methods in molecular biology (Clifton, N.J.) · 2025Review
- A Multi-Omics, Machine Learning-Aware, Genome-Wide Metabolic Model of Bacillus Subtilis Refines the Gene Expression and Cell Growth Prediction.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2024Article
- Identification ofAging · 2024Article
- Deep learning for protein secondary structure prediction: Pre and post-AlphaFold.Computational and structural biotechnology journal · 2022Review
- MFS-Unet: A Multi-Path Vision Mamba Network for Precise Thyroid Nodule Segmentation.IET systems biologyArticle
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2 authors.
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Abstract
Protein secondary structure prediction is extremely important for determining the spatial structure and function of proteins. In this paper, we apply an optimized convolutional neural network and long short-term memory neural network models to protein secondary structure prediction, which is called OCLSTM. We use an optimized convolutional neural network to extract local features between amino acid residues. Then use the bidirectional long short-term memory neural network to extract the remote interactions between the internal residues of the protein sequence to predict the protein structure. Experiments are performed on CASP10, CASP11, CASP12, CB513, and 25PDB datasets, and the good performance of 84.68%, 82.36%, 82.91%, 84.21% and 85.08% is achieved respectively. Experimental results show that the model can achieve better results.
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