What is he best known for?
The fields of study he is best known for:
Mark Borodovsky focuses on Gene, Genome, Gene prediction, Genetics and Computational biology.
His research investigates the link between Gene and topics such as Algorithm that cross with problems in Expressed sequence tag, Coding and Coding region.
His Genome research is multidisciplinary, incorporating perspectives in Regulatory sequence, Bioinformatics and Phylogenetic tree.
His study in Gene prediction is interdisciplinary in nature, drawing from both Prunus, Genomics, DNA sequencing, Fragaria and Hidden Markov model.
His Genetics research incorporates elements of Paramecium bursaria and Botany.
His Computational biology research includes elements of Sequence alignment, Markov chain, Bacterial genome size, Vertebrate and Genome Annotation Project and Open reading frame.
His most cited work include:
- The complete genome sequence of the gastric pathogen Helicobacter pylori (3247 citations)
- NCBI prokaryotic genome annotation pipeline (2119 citations)
- cag, a pathogenicity island of Helicobacter pylori, encodes type I-specific and disease-associated virulence factors. (1726 citations)
What are the main themes of his work throughout his whole career to date?
The scientist’s investigation covers issues in Gene, Genome, Genetics, Computational biology and Gene prediction.
His study brings together the fields of Identification and Gene.
His research on Genome focuses in particular on Genome project.
His research in Computational biology intersects with topics in Transcriptome, In silico, DNA sequencing and Bioinformatics.
His research on Gene prediction also deals with topics like
- Algorithm that intertwine with fields like Markov chain and Markov model,
- Hidden Markov model, which have a strong connection to Probabilistic logic and Machine learning.
His Sequence analysis study combines topics in areas such as Multiple sequence alignment, Theoretical computer science and Artificial intelligence.
He most often published in these fields:
- Gene (50.00%)
- Genome (47.01%)
- Genetics (40.30%)
What were the highlights of his more recent work (between 2014-2021)?
- Genome (47.01%)
- Gene prediction (37.31%)
- Computational biology (36.57%)
In recent papers he was focusing on the following fields of study:
His primary areas of investigation include Genome, Gene prediction, Computational biology, Gene and Genome project.
In his research on the topic of Genome, Eukaryotic genome is strongly related with Annotation.
His Gene prediction study deals with the bigger picture of Genetics.
His Computational biology research is multidisciplinary, incorporating elements of Transcriptome, Single-nucleotide polymorphism and Identification.
His work on Genomic organization as part of general Gene study is frequently linked to Pipeline and Chlamydomonas reinhardtii, therefore connecting diverse disciplines of science.
His Genome project research integrates issues from Reference genome, Bioinformatics and Exon.
Between 2014 and 2021, his most popular works were:
- NCBI prokaryotic genome annotation pipeline (2119 citations)
- BRAKER1: Unsupervised RNA-Seq-Based Genome Annotation with GeneMark-ET and AUGUSTUS (502 citations)
- BRAKER1: Unsupervised RNA-Seq-Based Genome Annotation with GeneMark-ET and AUGUSTUS (502 citations)
In his most recent research, the most cited papers focused on:
Mark Borodovsky spends much of his time researching Gene prediction, Gene, Genome, Computational biology and Genome project.
His research on Gene prediction concerns the broader Genetics.
His research related to Reference genome, Vertebrate and Genome Annotation Project and Cancer genome sequencing might be considered part of Genome.
His studies in Computational biology integrate themes in fields like Genomic organization, Promoter, Sequence motif, Operon and Protein sequencing.
His Genome project research is multidisciplinary, incorporating perspectives in Genome evolution, Molecular Sequence Annotation, ENCODE and Bioinformatics.
His biological study spans a wide range of topics, including Machine learning, RNA-Seq and Task, Artificial intelligence.
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