What is he best known for?
The fields of study he is best known for:
Haig H. Kazazian spends much of his time researching Genetics, Retrotransposon, Human genome, Gene and Genome.
His study involves Transposable element, Nucleic acid sequence, DNA, Allele and genomic DNA, a branch of Genetics.
The Retrotransposon study combines topics in areas such as RNA, Reverse transcriptase, Long interspersed nuclear element, Open reading frame and Consensus sequence.
Haig H. Kazazian interconnects Insertional mutagenesis, RNA interference, Transgene and Somatic cell in the investigation of issues within Human genome.
Haig H. Kazazian has researched Gene in several fields, including Molecular biology and Mutation.
His research in Genome intersects with topics in Evolutionary biology, Disease and Untranslated region.
His most cited work include:
- Mobile Elements: Drivers of Genome Evolution (1497 citations)
- Human L1 Retrotransposon Encodes a Conserved Endonuclease Required for Retrotransposition (898 citations)
- High Frequency Retrotransposition in Cultured Mammalian Cells (820 citations)
What are the main themes of his work throughout his whole career to date?
Genetics, Gene, Retrotransposon, Molecular biology and Human genome are his primary areas of study.
His study in Genome, Mutation, Allele, Genetic linkage and Globin falls within the category of Genetics.
His research on Gene often connects related areas such as DNA.
His Retrotransposon research includes themes of Long interspersed nuclear element, Genome evolution, RNA and Somatic cell.
Haig H. Kazazian usually deals with Molecular biology and limits it to topics linked to Thalassemia and Prenatal diagnosis.
Many of his studies on Human genome involve topics that are commonly interrelated, such as Mobile genetic elements.
He most often published in these fields:
- Genetics (64.18%)
- Gene (34.63%)
- Retrotransposon (26.27%)
What were the highlights of his more recent work (between 2005-2021)?
- Genetics (64.18%)
- Retrotransposon (26.27%)
- Human genome (15.22%)
In recent papers he was focusing on the following fields of study:
Haig H. Kazazian mainly investigates Genetics, Retrotransposon, Human genome, Genome and Somatic cell.
Genome evolution, Gene, Pseudogene, Ribonucleoprotein and Gene duplication are among the areas of Genetics where the researcher is concentrating his efforts.
His Retrotransposon research integrates issues from Insertional mutagenesis, Long interspersed nuclear element, RNA, Cell biology and Alu element.
As a member of one scientific family, Haig H. Kazazian mostly works in the field of Human genome, focusing on Transposable element and, on occasion, Mobile genetic elements, Endogenous retrovirus and Disease.
Haig H. Kazazian has included themes like Evolutionary biology, Genome instability and DNA, DNA sequencing in his Genome study.
His Somatic cell study which covers Cancer that intersects with Cancer research.
Between 2005 and 2021, his most popular works were:
- Retrotransposons Revisited: The Restraint and Rehabilitation of Parasites (484 citations)
- Active human retrotransposons: variation and disease. (431 citations)
- L1 retrotransposition is suppressed by endogenously encoded small interfering RNAs in human cultured cells (336 citations)
In his most recent research, the most cited papers focused on:
His scientific interests lie mostly in Retrotransposon, Genetics, Human genome, Genome and Somatic cell.
His Retrotransposon study integrates concerns from other disciplines, such as Insertional mutagenesis, Ribonucleoprotein, Molecular biology, RNA interference and Cell biology.
His studies in Alu element, Gene, Long interspersed nuclear element, RNA and Pseudogene are all subfields of Genetics research.
His Human genome study incorporates themes from Genome evolution, Computational biology and Transgene.
His Genome research includes elements of Evolutionary biology and Exon shuffling.
His Somatic cell study combines topics in areas such as Immunohistochemistry, Progressive disease, Cell culture and Adenocarcinoma.
This overview was generated by a machine learning system which analysed the scientist’s
body of work. If you have any feedback, you can
contact us
here.
