What is he best known for?
The fields of study he is best known for:
His primary areas of investigation include Bioinformatics, LiveBench, Data mining, CAFASP and Protein structure.
Leszek Rychlewski interconnects Computational biology, Multiple sequence alignment, Sequence alignment and Artificial intelligence in the investigation of issues within Bioinformatics.
His research on LiveBench concerns the broader Protein structure prediction.
As part of the same scientific family, Leszek Rychlewski usually focuses on Protein structure prediction, concentrating on Machine learning and intersecting with Snapshot.
His CAFASP research focuses on Server and how it relates to Database.
His Protein structure research includes elements of Sequence analysis and Protein methods.
His most cited work include:
- The Phaeodactylum genome reveals the evolutionary history of diatom genomes (1151 citations)
- 3D-Jury: a simple approach to improve protein structure predictions (690 citations)
- ELM server: a new resource for investigating short functional sites in modular eukaryotic proteins (563 citations)
What are the main themes of his work throughout his whole career to date?
His main research concerns Genetics, Computational biology, Peptide sequence, Protein structure and Protein structure prediction.
Leszek Rychlewski works mostly in the field of Computational biology, limiting it down to topics relating to Bioinformatics and, in certain cases, Protein sequencing.
Leszek Rychlewski has researched Peptide sequence in several fields, including Restriction enzyme and Active site.
His Protein structure research includes themes of Protein Data Bank and Protein folding.
Leszek Rychlewski studied Protein structure prediction and Machine learning that intersect with 3D-Jury.
His work deals with themes such as Server, Data mining and Artificial intelligence, which intersect with LiveBench.
He most often published in these fields:
- Genetics (27.33%)
- Computational biology (24.00%)
- Peptide sequence (17.33%)
What were the highlights of his more recent work (between 2010-2021)?
- Computational biology (24.00%)
- Genetics (27.33%)
- Biochemistry (12.67%)
In recent papers he was focusing on the following fields of study:
The scientist’s investigation covers issues in Computational biology, Genetics, Biochemistry, Genome and Enzyme.
Leszek Rychlewski has included themes like Transcription factor II A, Neuroscience, Transcription factor II D and TAF2 in his Computational biology study.
Leszek Rychlewski merges Genetics with Architecture domain in his research.
As a part of the same scientific study, he usually deals with the Biochemistry, concentrating on Stereochemistry and frequently concerns with Binding site, Active site and Thermostability.
His study deals with a combination of Genome and Thalassiosira pseudonana.
He works in the field of Peptide sequence, namely Sequence alignment.
Between 2010 and 2021, his most popular works were:
- Alphaherpesvirinae and Gammaherpesvirinae glycoprotein L and CMV UL130 originate from chemokines (185 citations)
- Sequence, structure and functional diversity of PD-(D/E)XK phosphodiesterase superfamily (68 citations)
- VoteDock: Consensus Docking Method for Prediction of Protein–Ligand Interactions (67 citations)
In his most recent research, the most cited papers focused on:
Biochemistry, Stereochemistry, Phenylacetone monooxygenase, Mixed Function Oxygenases and Peptide sequence are his primary areas of study.
His Stereochemistry research incorporates elements of Squalene monooxygenase, Saccharomyces cerevisiae, Yeast and Enzyme.
His Phenylacetone monooxygenase research overlaps with other disciplines such as Sequence analysis, Rhodococcus, Genome, Sequence motif and Thermostability.
Sequence analysis is closely attributed to Computational biology in his work.
The various areas that Leszek Rychlewski examines in his Computational biology study include Data mining and Ligand.
His work on Sequence alignment as part of his general Peptide sequence study is frequently connected to Architecture domain, thereby bridging the divide between different branches of science.
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