ArticlePloS one2012
A unified multitask architecture for predicting local protein properties.
Article in PloS one, 2012. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers, 1 of them a synthesis that pooled it.
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Who cites it
18 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
- Deep learning for protein secondary structure prediction: Pre and post-AlphaFold.Computational and structural biotechnology journal · 2022Review
- Machine-Learned Molecular Surface and Its Application to Implicit Solvent Simulations.Journal of chemical theory and computation · 2021Article
- Applications of artificial intelligence to drug design and discovery in the big data era: a comprehensive review.Molecular diversity · 2021Review
- Boosting the accuracy of protein secondary structure prediction through nearest neighbor search and method hybridization.Bioinformatics (Oxford, England) · 2020Article
- Precise Modeling of the Protective Effects of Quercetin against Mycotoxin via System Identification with Neural Networks.International journal of molecular sciences · 2019Article
- Multi-trait, Multi-environment Deep Learning Modeling for Genomic-Enabled Prediction of Plant Traits.G3 (Bethesda, Md.) · 2018Article
- Sixty-five years of the long march in protein secondary structure prediction: the final stretch?Briefings in bioinformatics · 2018Article
- Opportunities and obstacles for deep learning in biology and medicine.Journal of the Royal Society, Interface · 2018Review
- Article
- Predicting the Functional Impact of KCNQ1 Variants of Unknown Significance.Circulation. Cardiovascular genetics · 2017Article
- A neural network multi-task learning approach to biomedical named entity recognition.BMC bioinformatics · 2017Article
- Deep Artificial Neural Networks and Neuromorphic Chips for Big Data Analysis: Pharmaceutical and Bioinformatics Applications.International journal of molecular sciences · 2016Review
- Deep Learning for Population Genetic Inference.PLoS computational biology · 2016Article
- Improving prediction of secondary structure, local backbone angles, and solvent accessible surface area of proteins by iterative deep learning.Scientific reports · 2015Article
- Synthetic biology for the directed evolution of protein biocatalysts: navigating sequence space intelligently.Chemical Society reviews · 2015Review
- AcconPred: Predicting Solvent Accessibility and Contact Number Simultaneously by a Multitask Learning Framework under the Conditional Neural Fields Model.BioMed research international · 2015Article
- A Deep Learning Network Approach to ab initio Protein Secondary Structure Prediction.IEEE/ACM transactions on computational biology and bioinformaticsArticle
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Authors and funding
4 authors.
Funding
Abstract
A variety of functionally important protein properties, such as secondary structure, transmembrane topology and solvent accessibility, can be encoded as a labeling of amino acids. Indeed, the prediction of such properties from the primary amino acid sequence is one of the core projects of computational biology. Accordingly, a panoply of approaches have been developed for predicting such properties; however, most such approaches focus on solving a single task at a time. Motivated by recent, successful work in natural language processing, we propose to use multitask learning to train a single, joint model that exploits the dependencies among these various labeling tasks. We describe a deep neural network architecture that, given a protein sequence, outputs a host of predicted local properties, including secondary structure, solvent accessibility, transmembrane topology, signal peptides and DNA-binding residues. The network is trained jointly on all these tasks in a supervised fashion, augmented with a novel form of semi-supervised learning in which the model is trained to distinguish between local patterns from natural and synthetic protein sequences. The task-independent architecture of the network obviates the need for task-specific feature engineering. We demonstrate that, for all of the tasks that we considered, our approach leads to statistically significant improvements in performance, relative to a single task neural network approach, and that the resulting model achieves state-of-the-art performance.
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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.