ArticleBiophysical reviews2020
Neural networks for protein structure and function prediction and dynamic analysis.
Article in Biophysical reviews, 2020. 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.
- Protein Science Meets Artificial Intelligence: A Systematic Review and a Biochemical Meta-Analysis of an Inter-Field.Frontiers in bioengineering and biotechnology · 2022Pooled it
- Revealing SARS-CoV-2 MComputational and structural biotechnology journal · 2024Article
- ResisenseNet hybrid neural network model for predicting drug sensitivity and repurposing in breast Cancer.Scientific reports · 2024Article
- AI-Driven Deep Learning Techniques in Protein Structure Prediction.International journal of molecular sciences · 2024Review
- Energy Profile Bayes and Thompson Optimized Convolutional Neural Network protein structure prediction.Neural computing & applications · 2023Article
- Resolving Dynamics in the Ensemble: Finding Paths through Intermediate States and Disordered Protein Structures.The journal of physical chemistry. B · 2021Article
- Artificial intelligence and machine learning-aided drug discovery in central nervous system diseases: State-of-the-arts and future directions.Medicinal research reviews · 2021Review
- Biophysical Reviews: 2020-looking back, going forward.Biophysical reviews · 2020Article
- Overview of the big data bioinformatics symposium (2SCA) at BSJ2019.Biophysical reviews · 2020Article
- Biophysical Reviews' national biophysical society partnership program.Biophysical reviews · 2020Article
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Authors and funding
2 authors.
Funding
Abstract
Hardware and software advancements along with the accumulation of large amounts of data in recent years have together spurred a remarkable growth in the application of neural networks to various scientific fields. Machine learning based on neural networks with multiple (hidden) layers is becoming an extremely powerful approach for analyzing data. With the accumulation of large amounts of protein data such as structural and functional assay data, the effects of such approaches within the field of protein informatics are increasing. Here, we introduce our recent studies based on applications of neural networks for protein structure and function prediction and dynamic analysis involving: (i) inter-residue contact prediction based on a multiple sequence alignment (MSA) of amino acid sequences, (ii) prediction of protein-compound interaction using assay data, and (iii) detection of protein allostery from trajectories of molecular dynamic (MD) simulation.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.