ArticleIEEE/ACM transactions on computational biology and bioinformatics
A Deep Learning Network Approach to ab initio Protein Secondary Structure Prediction.
Article in IEEE/ACM transactions on computational biology and bioinformatics. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 75 papers.
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Who cites it
75 citing papers in PubMed.
- New insights into translational research in Alzheimer's disease guided by artificial intelligence, computational and systems biology.Acta pharmaceutica Sinica. B · 2025Review
- Artificial intelligence in orthopedics: fundamentals, current applications, and future perspectives.Military Medical Research · 2025Review
- Improving Protein Secondary Structure Prediction by Deep Language Models and Transformer Networks.Methods in molecular biology (Clifton, N.J.) · 2025Article
- MHTAPred-SS: A Highly Targeted Autoencoder-Driven Deep Multi-Task Learning Framework for Accurate Protein Secondary Structure Prediction.International journal of molecular sciences · 2024Article
- A Survey on Computational Methods in Drug Discovery for Neurodegenerative Diseases.Biomolecules · 2024Review
- Deep learning in structural bioinformatics: current applications and future perspectives.Briefings in bioinformatics · 2024Review
- Deep Learning for Genomics: From Early Neural Nets to Modern Large Language Models.International journal of molecular sciences · 2023Review
- Effective Local and Secondary Protein Structure Prediction by Combining a Neural Network-Based Approach with Extensive Feature Design and Selection without Reliance on Evolutionary Information.International journal of molecular sciences · 2023Article
- Protein Structure Prediction: Challenges, Advances, and the Shift of Research Paradigms.Genomics, proteomics & bioinformatics · 2023Review
- Rise of Deep Learning Clinical Applications and Challenges in Omics Data: A Systematic Review.Diagnostics (Basel, Switzerland) · 2023Review
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- Artificial Intelligence and Machine Learning Technology Driven Modern Drug Discovery and Development.International journal of molecular sciences · 2023Review
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- MutaGAN: A sequence-to-sequence GAN framework to predict mutations of evolving protein populations.Virus evolution · 2023Article
- Energy Profile Bayes and Thompson Optimized Convolutional Neural Network protein structure prediction.Neural computing & applications · 2023Article
- Collectively encoding protein properties enriches protein language models.BMC bioinformatics · 2022Article
- PrMFTP: Multi-functional therapeutic peptides prediction based on multi-head self-attention mechanism and class weight optimization.PLoS computational biology · 2022Article
- Multi-level analysis of intrinsically disordered protein docking methods.Methods (San Diego, Calif.) · 2022Article
- Deep Ensemble Learning with Atrous Spatial Pyramid Networks for Protein Secondary Structure Prediction.Biomolecules · 2022Article
- Machine Learning in Drug Discovery: A Review.Artificial intelligence review · 2022Article
15 more citing papers are in PubMed but not listed here.
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3 authors.
Funding
Abstract
Ab initio protein secondary structure (SS) predictions are utilized to generate tertiary structure predictions, which are increasingly demanded due to the rapid discovery of proteins. Although recent developments have slightly exceeded previous methods of SS prediction, accuracy has stagnated around 80 percent and many wonder if prediction cannot be advanced beyond this ceiling. Disciplines that have traditionally employed neural networks are experimenting with novel deep learning techniques in attempts to stimulate progress. Since neural networks have historically played an important role in SS prediction, we wanted to determine whether deep learning could contribute to the advancement of this field as well. We developed an SS predictor that makes use of the position-specific scoring matrix generated by PSI-BLAST and deep learning network architectures, which we call DNSS. Graphical processing units and CUDA software optimize the deep network architecture and efficiently train the deep networks. Optimal parameters for the training process were determined, and a workflow comprising three separately trained deep networks was constructed in order to make refined predictions. This deep learning network approach was used to predict SS for a fully independent test dataset of 198 proteins, achieving a Q3 accuracy of 80.7 percent and a Sov accuracy of 74.2 percent.
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