What is he best known for?
The fields of study he is best known for:
- Gene
- Mechanical engineering
- DNA
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 most cited work include:
- Signatures of mutational processes in human cancer (5372 citations)
- Intratumor heterogeneity and branched evolution revealed by multiregion sequencing. (5361 citations)
- The rapid generation of mutation data matrices from protein sequences (4816 citations)
What are the main themes of his work throughout his whole career to date?
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.
He most often published in these fields:
- Computational biology (30.59%)
- Protein structure (29.12%)
- Artificial intelligence (26.76%)
What were the highlights of his more recent work (between 2016-2021)?
- Artificial intelligence (26.76%)
- Deep learning (13.24%)
- Artificial neural network (12.35%)
In recent papers he was focusing on the following fields of study:
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.
Between 2016 and 2021, his most popular works were:
- Improved protein structure prediction using potentials from deep learning (518 citations)
- Improved protein structure prediction using potentials from deep learning (518 citations)
- The PSIPRED Protein Analysis Workbench: 20 years on. (330 citations)
In his most recent research, the most cited papers focused on:
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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