Shaun M. Kandathil

发表

Prediction of interresidue contacts with DeepMetaPSICOV in CASP13

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

Toward a detailed understanding of search trajectories in fragment assembly approaches to protein structure prediction

Julia Handl, Simon C. Lovell, Shaun M. Kandathil, 2016, 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..

Generating, Maintaining, and Exploiting Diversity in a Memetic Algorithm for Protein Structure Prediction

Joshua D. Knowles, Mario Garza-Fabre, Julia Handl, 2016, Evolutionary Computation.

Using Machine Learning to Explore the Relevance of Local and Global Features During Conformational Search in Rosetta

Joshua D. Knowles, Mario Garza-Fabre, Julia Handl, 2015, GECCO.

Accuracy and tractability of a kriging model of intramolecular polarizable multipolar electrostatics and its application to histidine

Joshua D. Knowles, Yongna Yuan, Paul L. A. Popelier, 2013, J. Comput. Chem..

Deep learning extends de novo protein modelling coverage of genomes using iteratively predicted structural constraints

David T. Jones, Shaun M. Kandathil, David T. Jones, 2018, Nature Communications.

DMPfold: fast de novo protein model generation from covarying sequences using predicted distances and iterative model building

David T. Jones, Shaun M. Kandathil, Joe G Greener, 2018 .

On heuristic bias in fragment-assembly methods for protein structure prediction

Mario Garza-Fabre, Julia Handl, Simon C. Lovell, 2017, GECCO.

Ultrafast end-to-end protein structure prediction enables high-throughput exploration of uncharacterised proteins

Shaun M. Kandathil, Joe G Greener, Andy M. Lau, 2021 .

Near-complete protein structural modelling of the minimal genome.

Shaun M. Kandathil, David T. Jones, Joe G. Greener, 2020, 2007.06623.

Deep learning extends de novo protein modelling coverage of genomes using iteratively predicted structural constraints

Shaun M. Kandathil, David T. Jones, Joe G. Greener, 2018, Nature Communications.

Sequence Covariation Analysis in Biological Polymers

Shaun M. Kandathil, David T. Jones, W. Taylor, 2019, Handbook of Statistical Genomics.

A review of the chemistry and pharmacology of the date fruits (Phoenix dactylifera L.)

Shaun M. Kandathil, M. Baliga, H. Bhat, 2011 .

Proton tunnelling and promoting vibrations during the oxidation of ascorbate by ferricyanide?

Shaun M. Kandathil, N. Scrutton, R. Dunn, 2014, Physical chemistry chemical physics : PCCP.

A guide to machine learning for biologists

Shaun M. Kandathil, Joe G. Greener, Lewis Moffat, 2021, Nature Reviews Molecular Cell Biology.

A guide to machine learning for biologists

Shaun M. Kandathil, Joe G. Greener, Lewis Moffat, 2021, Nature Reviews Molecular Cell Biology.

Uncommon mutational profiles of metastatic colorectal cancer detected during routine genotyping using next generation sequencing

Shaun M. Kandathil, A. Leroux, J. Merlin, 2019, Scientific Reports.

Adaptive HIV-1 evolutionary trajectories are constrained by protein stability

Shaun M. Kandathil, S. Lovell, D. Robertson, 2017, Virus evolution.

Design in the DARK: Learning Deep Generative Models for De Novo Protein Design

Shaun M. Kandathil, Lewis Moffat, David T. Jones, 2022, bioRxiv.

University of Birmingham Generating, maintaining and exploiting diversity in a memetic algorithm for protein structure prediction

Shaun M. Kandathil, S. Lovell, J. Handl, 2016 .

Reliable Generation of Native-Like Decoys Limits Predictive Ability in Fragment-Based Protein Structure Prediction

Mario Garza-Fabre, Julia Handl, Shaun M. Kandathil, 2019, Biomolecules.

Recent developments in deep learning applied to protein structure prediction

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

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.

Ultrafast end-to-end protein structure prediction enables high-throughput exploration of uncharacterized proteins

Shaun M. Kandathil, Andy M. Lau, David T. Jones, 2022, Proceedings of the National Academy of Sciences.

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

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

Improved fragment-based protein structure prediction by redesign of search heuristics

Mario Garza-Fabre, Julia Handl, Shaun M. Kandathil, 2018, Scientific Reports.