ArticleFrontiers in bioengineering and biotechnology2021
Protein Secondary Structure Prediction With a Reductive Deep Learning Method.
Article in Frontiers in bioengineering and biotechnology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.
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15 citing papers in PubMed.
- Deep learning architectures achieve state-of-the-art (SOTA) accuracy in protein secondary structure prediction.Molecular diversity · 2026Review
- Combining knowledge distillation and neural networks to predict protein secondary structure.Scientific reports · 2025Article
- Recent Advances in Computational Prediction of Secondary and Supersecondary Structures from Protein Sequences.Methods in molecular biology (Clifton, N.J.) · 2025Review
- Importance of Secondary Structure Data in Large Scale Protein Modeling Using Low-Resolution SURPASS Method.Methods in molecular biology (Clifton, N.J.) · 2025Article
- Post-processing enhances protein secondary structure prediction with second order deep learning and embeddings.Computational and structural biotechnology journal · 2025Article
- Prediction of protein secondary structure by the improved TCN-BiLSTM-MHA model with knowledge distillation.Scientific reports · 2024Article
- An In silico Approach towards Finding the Cancer-Causing Mutations in Human MET Gene.International journal of genomics · 2023Article
- DLBLS_SS: protein secondary structure prediction using deep learning and broad learning system.RSC advances · 2022Article
- Measuring depression severity based on facial expression and body movement using deep convolutional neural network.Frontiers in psychiatry · 2022Article
- Cross-Attention and Deep Supervision UNet for Lesion Segmentation of Chronic Stroke.Frontiers in neuroscience · 2022Article
- Multistage Combination Classifier Augmented Model for Protein Secondary Structure Prediction.Frontiers in genetics · 2022Article
- Deep learning for protein secondary structure prediction: Pre and post-AlphaFold.Computational and structural biotechnology journal · 2022Review
- Protein pACS omega · 2021Article
- A Review of Methods for Sleep Arousal Detection Using Polysomnographic Signals.Brain sciences · 2021Review
- ECM-LSE: Prediction of Extracellular Matrix Proteins Using Deep Latent Space Encoding of k-Spaced Amino Acid Pairs.Frontiers in bioengineering and biotechnology · 2021Article
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5 authors.
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Abstract
Protein secondary structures have been identified as the links in the physical processes of primary sequences, typically random coils, folding into functional tertiary structures that enable proteins to involve a variety of biological events in life science. Therefore, an efficient protein secondary structure predictor is of importance especially when the structure of an amino acid sequence fragment is not solved by high-resolution experiments, such as X-ray crystallography, cryo-electron microscopy, and nuclear magnetic resonance spectroscopy, which are usually time consuming and expensive. In this paper, a reductive deep learning model MLPRNN has been proposed to predict either 3-state or 8-state protein secondary structures. The prediction accuracy by the MLPRNN on the publicly available benchmark CB513 data set is comparable with those by other state-of-the-art models. More importantly, taking into account the reductive architecture, MLPRNN could be a baseline for future developments.
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