What is he best known for?
The fields of study he is best known for:
His scientific interests lie mostly in Genetics, Computational biology, Protein structure, Phylogenetic tree and Phylogenetics.
Tal Pupko combines subjects such as Amino acid, Identification and Function with his study of Computational biology.
His Function research incorporates elements of Biochemistry and Multiple sequence alignment.
His study in Protein structure is interdisciplinary in nature, drawing from both Protein Biochemistry, Biophysics, Sequence analysis and Conserved sequence.
His research investigates the connection with Phylogenetic tree and areas like Data mining which intersect with concerns in Tree, Robustness, Inference and Statistical hypothesis testing.
His research in Phylogenetics intersects with topics in Entomology and Evolutionary biology.
His most cited work include:
- ConSurf 2010: calculating evolutionary conservation in sequence and structure of proteins and nucleic acids (1305 citations)
- ConSurf 2016: an improved methodology to estimate and visualize evolutionary conservation in macromolecules. (1042 citations)
- ConSurf 2005: the projection of evolutionary conservation scores of residues on protein structures (1037 citations)
What are the main themes of his work throughout his whole career to date?
Tal Pupko mainly investigates Genetics, Computational biology, Phylogenetics, Phylogenetic tree and Gene.
His studies examine the connections between Genetics and genetics, as well as such issues in Inference, with regards to Bioinformatics, Probabilistic logic and Pattern recognition.
His Computational biology research incorporates themes from Protein structure, Amino acid and Epitope, Antibody.
The concepts of his Protein structure study are interwoven with issues in Sequence analysis and Conserved sequence.
His Phylogenetics research integrates issues from Evolutionary biology and Function.
His work on Phyletic gradualism as part of general Phylogenetic tree study is frequently connected to Set, therefore bridging the gap between diverse disciplines of science and establishing a new relationship between them.
He most often published in these fields:
- Genetics (49.29%)
- Computational biology (27.86%)
- Phylogenetics (30.71%)
What were the highlights of his more recent work (between 2018-2021)?
- Algorithm (15.71%)
- Computational biology (27.86%)
- Inference (16.43%)
In recent papers he was focusing on the following fields of study:
His primary scientific interests are in Algorithm, Computational biology, Inference, Phylogenetic tree and Model selection.
Tal Pupko works mostly in the field of Algorithm, limiting it down to topics relating to Statistical model and, in certain cases, Clade, Task and Biological data.
His work deals with themes such as Amino acid, Proteome and DNA sequencing, which intersect with Computational biology.
Tal Pupko has researched Inference in several fields, including Tree, Evolutionary dynamics and Probabilistic logic.
His Tree research is multidisciplinary, incorporating perspectives in Multiple sequence alignment and Sequence alignment.
His research integrates issues of Statistical inference, Phylogenetics and Selection in his study of Model selection.
Between 2018 and 2021, his most popular works were:
- Model selection may not be a mandatory step for phylogeny reconstruction (75 citations)
- M1CR0B1AL1Z3R—a user-friendly web server for the analysis of large-scale microbial genomics data (21 citations)
- Multiple Sequence Alignment Averaging Improves Phylogeny Reconstruction. (11 citations)
In his most recent research, the most cited papers focused on:
Tal Pupko focuses on Algorithm, Phylogenetic tree, Sequence reconstruction, Model selection and Network topology.
His Algorithm study integrates concerns from other disciplines, such as Multiple sequence alignment and Inference.
His Phylogenetic tree research includes elements of Comparative genomics, Genomics, Sequence alignment and Bacterial genome size.
His Sequence reconstruction research overlaps with Simulated data, Phylogenetics and Rendering.
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