What is he best known for?
The fields of study he is best known for:
His primary areas of study are Genetics, Molecular biology, Gene, Mitochondrion and Mitochondrial DNA.
Allele frequency, Haplotype, Consanguinity, Ichthyosis and Locus are the primary areas of interest in his Genetics study.
His Molecular biology research is multidisciplinary, relying on both Chromosome 13, Chromosome 9, TCF4 and Complementary DNA.
His research integrates issues of Carcinogenesis, Cancer cell and Oxidative phosphorylation in his study of Mitochondrion.
His studies in Mitochondrial DNA integrate themes in fields like Mutation and Cancer research.
His Mutation research integrates issues from Phenotype, Nuclear DNA and Proto-Oncogene Proteins c-ret.
His most cited work include:
- Identification of a novel X-linked gene responsible for Emery-Dreifuss muscular dystrophy. (762 citations)
- Disruptive mitochondrial DNA mutations in complex I subunits are markers of oncocytic phenotype in thyroid tumors (213 citations)
- Consanguineous marriages, pearls and perils: Geneva International Consanguinity Workshop Report (174 citations)
What are the main themes of his work throughout his whole career to date?
Giovanni Romeo mainly investigates Genetics, Gene, Molecular biology, Mitochondrial DNA and Mutation.
His Genetics research focuses on Haplotype, Locus, Single-nucleotide polymorphism, Genetic linkage and Exome sequencing.
His Haplotype study which covers Allele frequency that intersects with Linkage disequilibrium and Consanguinity.
His biological study spans a wide range of topics, including Chromosome, Biochemistry and Restriction fragment length polymorphism.
His research in Mitochondrial DNA intersects with topics in Cancer research, Mitochondrion and Frameshift mutation.
His work in Mutation addresses subjects such as Phenotype, which are connected to disciplines such as Thyroid.
He most often published in these fields:
- Genetics (60.51%)
- Gene (17.20%)
- Molecular biology (17.83%)
What were the highlights of his more recent work (between 2011-2021)?
- Genetics (60.51%)
- Exome sequencing (7.01%)
- Mitochondrial DNA (12.74%)
In recent papers he was focusing on the following fields of study:
Giovanni Romeo mainly focuses on Genetics, Exome sequencing, Mitochondrial DNA, Mutation and Allele.
His study in Intellectual disability, Gene, Chromosome, Allele frequency and Consanguinity are all subfields of Genetics.
His Chromosome research is multidisciplinary, incorporating perspectives in Molecular biology, Human genetics and Haplogroup H.
In his research on the topic of Mitochondrial DNA, Immunoglobulin superfamily and CHL1 is strongly related with Genome.
His Mutation study incorporates themes from Phenotype, Cancer research and Cell biology.
His Allele research incorporates elements of Lactase persistence, Genetic variation and Genotype.
Between 2011 and 2021, his most popular works were:
- EXCAVATOR: detecting copy number variants from whole-exome sequencing data (173 citations)
- Relevance of mitochondrial genetics and metabolism in cancer development. (75 citations)
- H3M2: detection of runs of homozygosity from whole-exome sequencing data. (61 citations)
In his most recent research, the most cited papers focused on:
Genetics, Mutation, Intellectual disability, 1000 Genomes Project and Mitochondrial DNA are his primary areas of study.
Allele, Genome, Thyroid cancer, Copy-number variation and Single-nucleotide polymorphism are among the areas of Genetics where the researcher is concentrating his efforts.
Thyroid, Thyroid carcinoma, Tumor suppressor gene, Phenotype and Reverse transcription polymerase chain reaction is closely connected to Cancer research in his research, which is encompassed under the umbrella topic of Mutation.
Giovanni Romeo combines topics linked to Gene with his work on Intellectual disability.
The concepts of his 1000 Genomes Project study are interwoven with issues in DNA microarray and Human genome.
His work on Heteroplasmy as part of general Mitochondrial DNA study is frequently connected to Context, therefore bridging the gap between diverse disciplines of science and establishing a new relationship between them.
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.
