David T. Jones

John B. O. Mitchell, David T. Jones, J. Thornton, 1993, Philosophical Transactions of the Royal Society of London. Series A: Physical and Engineering Sciences.

Membrane protein orientation and refinement using a knowledge-based statistical potential

David T. Jones, Timothy Nugent, 2013, BMC Bioinformatics.

Predicting human protein function with multi-task deep neural networks

Rui Fa, David T Jones, Domenico Cozzetto, 2018, bioRxiv.

FFPred: an integrated feature-based function prediction server for vertebrate proteomes

Christine A. Orengo, David T. Jones, Timothy Nugent, 2008, Nucleic Acids Res..

Prediction of interresidue contacts with DeepMetaPSICOV in CASP13

David T Jones, Joe G Greener, David T. Jones, 2019, Proteins.

High precision in protein contact prediction using fully convolutional neural networks and minimal sequence features

David T. Jones, Shaun M. Kandathil, David T. Jones, 2018, Bioinform..

Modularity of intrinsic disorder in the human proteome

David T. Jones, David T Jones, M. Pentony, 2010, Proteins.

Improving sequence-based fold recognition by using 3D model quality assessment

Liam J. McGuffin, David T. Jones, David T. Jones, 2005, Bioinform..

The Genomic Threading Database: a comprehensive resource for structural annotations of the genomes from key organisms

Liam J. McGuffin, Stefano A. Street, Kevin Bryson, 2004, Nucleic Acids Res..

Differentiable molecular simulation can learn all the parameters in a coarse-grained force field for proteins

Joe G Greener, David T. Jones, Joe G. Greener, 2021, bioRxiv.

Protein function prediction is improved by creating synthetic feature samples with generative adversarial networks

David T. Jones, Cen Wan, David T. Jones, 2020, Nature Machine Intelligence.

What are the baselines for protein fold recognition?

Liam J. McGuffin, Kevin Bryson, David T. Jones, 2001, Bioinform..

De novo protein design using pairwise potentials and a genetic algorithm

David T. Jones, David T. Jones, 1994, Protein science : a publication of the Protein Society.

FFPred 3: feature-based function prediction for all Gene Ontology domains

Hannah Currant, David T. Jones, Domenico Cozzetto, 2016, Scientific Reports.

Evaluation of predictions in the CASP10 model refinement category

David T Jones, Domenico Cozzetto, Timothy Nugent, 2013, Proteins.

Merizo: a rapid and accurate domain segmentation method using invariant point attention

Andy M. Lau, David T. Jones, S. Kandathil, 2023, bioRxiv.

Predictions of Hot Spot Residues at Protein-Protein Interfaces Using Support Vector Machines

Daniel W. A. Buchan, David T. Jones, M. Pontil, 2011, PloS one.

Recent developments in deep learning applied to protein structure prediction

David T Jones, Shaun M. Kandathil, Joe G Greener, 2019, Proteins.

AlphaFold: Improved protein structure prediction using

David T. Jones, L. Sifre, K. Kavukcuoglu, 2019 .

springScape: visualisation of microarray and contextual bioinformatic data using spring embedding and an "information landscape"

Timothy M. D. Ebbels, Bernard F. Buxton, David T. Jones, 2006, ISMB.

EigenTHREADER: analogous protein fold recognition by efficient contact map threading

Daniel W. A. Buchan, David T. Jones, David T. Jones, 2017, Bioinform..

Protein function prediction by massive integration of evolutionary analyses and multiple data sources

Daniel W. A. Buchan, David T. Jones, D. Cozzetto, 2013, BMC Bioinformatics.

Learning a functional grammar of protein domains using natural language word embedding techniques

Daniel W. A. Buchan, David T. Jones, Daniel Wa Buchan, 2019, Proteins.

Computational Methods for Annotation Transfers from Sequence.

David T Jones, Domenico Cozzetto, David T. Jones, 2016, Methods in molecular biology.

The MEMPACK alpha-helical transmembrane protein structure prediction server

David T. Jones, Timothy Nugent, Sean Ward, 2011, Bioinform..

Improving classification in protein structure databases using text mining

David T. Jones, David T. Jones, Antonis Koussounadis, 2009, BMC Bioinformatics.

Improving protein function prediction with synthetic feature samples created by generative adversarial networks

David T. Jones, Cen Wan, David T. Jones, 2019, bioRxiv.

Crohn disease risk prediction—Best practices and pitfalls with exome data

David T. Jones, S. Tosatto, E. Leonardi, 2017, Human mutation.

Opportunities and limitations in applying coevolution-derived contacts to protein structure prediction

David T. Jones, David T. Jones, Tomasz Kosciolek, 2014, Bio Algorithms Med Syst..

Prediction of interresidue contacts with DeepMetaPSICOV in CASP13

David T. Jones, Shaun M. Kandathil, Joe G Greener, 2019, bioRxiv.

Machine learning methods for predicting protein structure from single sequences.

Shaun M. Kandathil, Andy M. Lau, David T. Jones, 2023, Current opinion in structural biology.

Protein Structure Prediction in Genomics

David T. Jones, David T. Jones, 2001, Briefings Bioinform..

Deep learning-based prediction of protein structure using learned representations of multiple sequence alignments

David T Jones, Shaun M. Kandathil, Joe G Greener, 2020 .

From GeneWeaver to Agmial

Michael Luck, Kevin Bryson, David T. Jones, 2002 .

Outcome of a workshop on applications of protein models in biomedical research.

Roland L. Dunbrack, David T. Jones, M. Levitt, 2009, Structure.