The scientist’s investigation covers issues in Genetics, Mutation, Protein structure, Cancer and Protein structure prediction. His work carried out in the field of Mutation brings together such families of science as Algorithm, Cancer research, Phylogenetics and Histone H3. His work deals with themes such as Computational biology, Structural alignment, Sequence alignment and Protein folding, which intersect with Protein structure.
His studies deal with areas such as Data mining and Artificial intelligence as well as Protein folding. His work on Carcinogenesis and Breast cancer is typically connected to PTEN as part of general Cancer study, connecting several disciplines of science. The concepts of his Protein structure prediction study are interwoven with issues in Artificial neural network, Bioinformatics and Functional genomics.
His scientific interests lie mostly in Computational biology, Protein structure, Artificial intelligence, Genetics and Protein structure prediction. His work in Computational biology addresses issues such as Protein function prediction, which are connected to fields such as Annotation. His Protein structure study combines topics from a wide range of disciplines, such as Bioinformatics, Sequence alignment, Protein secondary structure and Protein folding.
As a member of one scientific family, he mostly works in the field of Artificial intelligence, focusing on Algorithm and, on occasion, Sequence. His Protein structure prediction research incorporates themes from Data mining, Membrane protein and Structural bioinformatics. His study in Mutation is interdisciplinary in nature, drawing from both Cancer and Cancer research.
David T. Jones mainly investigates Artificial intelligence, Deep learning, Artificial neural network, Computational biology and Machine learning. David T. Jones interconnects Algorithm, Protein design and Protein folding in the investigation of issues within Artificial intelligence. The various areas that David T. Jones examines in his Deep learning study include Protein structure, Sequence, Biological data and Pattern recognition.
His Artificial neural network research includes elements of Function, Field and Protein structure prediction. His Computational biology research is multidisciplinary, incorporating perspectives in Protein function, Genome, Gene and Function. David T. Jones has included themes like Protein function prediction and Protein sequencing in his Machine learning study.
His primary areas of study are Artificial intelligence, Deep learning, Artificial neural network, Protein structure prediction and Protein structure. His Artificial intelligence study integrates concerns from other disciplines, such as Algorithm, Protein family and Protein folding. His research ties Computational biology and Deep learning together.
He works mostly in the field of Artificial neural network, limiting it down to topics relating to Peptide sequence and, in certain cases, Target protein, Threading and Homology, as a part of the same area of interest. In the field of Protein structure prediction, his study on CASP overlaps with subjects such as Task. His work on Protein design as part of general Protein structure research is often related to Process, thus linking different fields of science.
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Intratumor heterogeneity and branched evolution revealed by multiregion sequencing.
Marco Gerlinger;Andrew J. Rowan;Stuart Horswell;James Larkin.
The New England Journal of Medicine (2012)
Signatures of mutational processes in human cancer
Ludmil B. Alexandrov;Serena Nik-Zainal;Serena Nik-Zainal;David C. Wedge;Samuel A. J. R. Aparicio.
The rapid generation of mutation data matrices from protein sequences
David T. Jones;William R. Taylor;Janet M. Thornton.
PROTEIN SECONDARY STRUCTURE PREDICTION BASED ON POSITION-SPECIFIC SCORING MATRICES
David T Jones.
Journal of Molecular Biology (1999)
The PSIPRED protein structure prediction server.
Liam J. McGuffin;Kevin Bryson;David T. Jones.
Patterns of somatic mutation in human cancer genomes
Christopher Greenman;Philip Stephens;Raffaella Smith;Gillian L. Dalgliesh.
CATH – a hierarchic classification of protein domain structures
CA Orengo;AD Michie;S Jones;DT Jones.
Prediction and functional analysis of native disorder in proteins from the three kingdoms of life.
J. J. Ward;J. S. Sodhi;Liam J. McGuffin;B. F. Buxton.
Journal of Molecular Biology (2004)
Improved protein structure prediction using potentials from deep learning
Andrew W. Senior;Richard Evans;John Jumper;James Kirkpatrick.
A new approach to protein fold recognition.
DT Jones;WR Taylor;JM Thornton.
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