Multi-S3P: Protein Secondary Structure Prediction With Specialized Multi-Network and Self-Attention-Based Deep Learning Model
Author:
Affiliation:
1. School of Information and Communication Technology, Griffith University, Nathan, QLD, Australia
2. Institute for Integrated and Intelligent Systems, Griffith University, Nathan, QLD, Australia
Funder
Australian Research Council
Accelerating Higher Education Expansion and Development (AHEAD) Operations Project of Sri Lanka
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Subject
General Engineering,General Materials Science,General Computer Science,Electrical and Electronic Engineering
Link
http://xplorestaging.ieee.org/ielx7/6287639/10005208/10143539.pdf?arnumber=10143539
Reference74 articles.
1. Enhancing protein backbone angle prediction by using simpler models of deep neural networks
2. Accurate prediction of protein contact maps by coupling residual two-dimensional bidirectional long short-term memory with convolutional neural networks
3. Secondary structure specific simpler prediction models for protein backbone angles
4. SSpro/ACCpro 5: almost perfect prediction of protein secondary structure and relative solvent accessibility using profiles, machine learning and structural similarity
5. A stacked meta-ensemble for protein inter-residue distance prediction
Cited by 2 articles. 订阅此论文施引文献 订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献
1. Self-attention and asymmetric multi-layer perceptron-gated recurrent unit blocks for protein secondary structure prediction;Applied Soft Computing;2024-07
2. A Hybrid Bidirectional Long Short-Term Memory and Bidirectional Gated Recurrent Unit Architecture for Protein Secondary Structure Prediction;IEEE Access;2024
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