Structure-based protein function prediction using graph convolutional networks
Published 2021 View Full Article
- Home
- Publications
- Publication Search
- Publication Details
Title
Structure-based protein function prediction using graph convolutional networks
Authors
Keywords
-
Journal
Nature Communications
Volume 12, Issue 1, Pages -
Publisher
Springer Science and Business Media LLC
Online
2021-05-26
DOI
10.1038/s41467-021-23303-9
References
Ask authors/readers for more resources
Related references
Note: Only part of the references are listed.- Improved protein structure prediction using potentials from deep learning
- (2020) Andrew W. Senior et al. NATURE
- Cryo-Electron Microscopy: Moving Beyond X-Ray Crystal Structures for Drug Receptors and Drug Development
- (2019) Javier García-Nafría et al. Annual Review of Pharmacology and Toxicology
- Archiving and disseminating integrative structure models
- (2019) Brinda Vallat et al. JOURNAL OF BIOMOLECULAR NMR
- Electron cryo-microscopy for elucidating the dynamic nature of live-protein complexes
- (2019) Hideki Shigematsu BIOCHIMICA ET BIOPHYSICA ACTA-GENERAL SUBJECTS
- Deep learning extends de novo protein modelling coverage of genomes using iteratively predicted structural constraints
- (2019) Joe G. Greener et al. Nature Communications
- deepNF: deep network fusion for protein function prediction
- (2018) Vladimir Gligorijević et al. BIOINFORMATICS
- Methods for interpreting and understanding deep neural networks
- (2018) Grégoire Montavon et al. DIGITAL SIGNAL PROCESSING
- SWISS-MODEL: homology modelling of protein structures and complexes
- (2018) Andrew Waterhouse et al. NUCLEIC ACIDS RESEARCH
- EnzyNet: enzyme classification using 3D convolutional neural networks on spatial representation
- (2018) Afshine Amidi et al. PeerJ
- High Precision Protein Functional Site Detection Using 3D Convolutional Neural Networks
- (2018) Wen Torng et al. BIOINFORMATICS
- Towards region-specific propagation of protein functions
- (2018) Da Chen Emily Koo et al. BIOINFORMATICS
- InterPro in 2019: improving coverage, classification and access to protein sequence annotations
- (2018) Alex L Mitchell et al. NUCLEIC ACIDS RESEARCH
- Geometric Deep Learning: Going beyond Euclidean data
- (2017) Michael M. Bronstein et al. IEEE SIGNAL PROCESSING MAGAZINE
- Convolutional Embedding of Attributed Molecular Graphs for Physical Property Prediction
- (2017) Connor W. Coley et al. Journal of Chemical Information and Modeling
- Protein structure determination using metagenome sequence data
- (2017) Sergey Ovchinnikov et al. SCIENCE
- CATH: an expanded resource to predict protein function through structure and sequence
- (2016) Natalie L. Dawson et al. NUCLEIC ACIDS RESEARCH
- KEGG: new perspectives on genomes, pathways, diseases and drugs
- (2016) Minoru Kanehisa et al. NUCLEIC ACIDS RESEARCH
- Compact Integration of Multi-Network Topology for Functional Analysis of Genes
- (2016) Hyunghoon Cho et al. Cell Systems
- FFPred 3: feature-based function prediction for all Gene Ontology domains
- (2016) Domenico Cozzetto et al. Scientific Reports
- Functional classification of CATH superfamilies: a domain-based approach for protein function annotation
- (2015) Sayoni Das et al. BIOINFORMATICS
- Deep learning
- (2015) Yann LeCun et al. NATURE
- Predicting effects of noncoding variants with deep learning–based sequence model
- (2015) Jian Zhou et al. NATURE METHODS
- DISOPRED3: precise disordered region predictions with annotated protein-binding activity
- (2014) David T. Jones et al. BIOINFORMATICS
- A large-scale evaluation of computational protein function prediction
- (2013) Predrag Radivojac et al. NATURE METHODS
- Pfam: the protein families database
- (2013) Robert D. Finn et al. NUCLEIC ACIDS RESEARCH
- ModBase, a database of annotated comparative protein structure models and associated resources
- (2013) Ursula Pieper et al. NUCLEIC ACIDS RESEARCH
- CombFunc: predicting protein function using heterogeneous data sources
- (2012) Mark N. Wass et al. NUCLEIC ACIDS RESEARCH
- BioLiP: a semi-manually curated database for biologically relevant ligand–protein interactions
- (2012) Jianyi Yang et al. NUCLEIC ACIDS RESEARCH
- ResBoost: characterizing and predicting catalytic residues in enzymes
- (2009) Ron Alterovitz et al. BMC BIOINFORMATICS
- Predicting gene function in a hierarchical context with an ensemble of classifiers
- (2008) Yuanfang Guan et al. GENOME BIOLOGY
- A critical assessment of Mus musculus gene function prediction using integrated genomic evidence
- (2008) Lourdes Peña-Castillo et al. GENOME BIOLOGY
- GeneMANIA: a real-time multiple association network integration algorithm for predicting gene function
- (2008) Sara Mostafavi et al. GENOME BIOLOGY
Discover Peeref hubs
Discuss science. Find collaborators. Network.
Join a conversationBecome a Peeref-certified reviewer
The Peeref Institute provides free reviewer training that teaches the core competencies of the academic peer review process.
Get Started