What is he best known for?
The fields of study he is best known for:
His primary areas of study are Genetics, Haplotype, International HapMap Project, Single-nucleotide polymorphism and Tag SNP.
His research in Linkage disequilibrium, Genome-wide association study, Genome, Exome sequencing and Imputation are components of Genetics.
The concepts of his Linkage disequilibrium study are interwoven with issues in Schizophrenia, Psychosis, Haplotype estimation and Zinc finger protein 804A.
The study incorporates disciplines such as Computational biology and Genetic association in addition to Haplotype.
His Computational biology research incorporates elements of Multiple comparisons problem and Genotyping.
Itsik Pe'er works mostly in the field of International HapMap Project, limiting it down to topics relating to Selection and, in certain cases, Statistical power.
His most cited work include:
- A haplotype map of the human genome (5144 citations)
- A second generation human haplotype map of over 3.1 million SNPs (3819 citations)
- Efficiency and power in genetic association studies (1601 citations)
What are the main themes of his work throughout his whole career to date?
His primary areas of investigation include Genetics, Genome-wide association study, Computational biology, Haplotype and Single-nucleotide polymorphism.
His study in Genetic association, Identity by descent, Linkage disequilibrium, Allele frequency and Genome is carried out as part of his Genetics studies.
He has researched Genetic association in several fields, including Evolutionary biology and Multiple comparisons problem.
His work in Computational biology addresses issues such as Genomics, which are connected to fields such as Machine learning, Exome sequencing and Artificial intelligence.
His Haplotype research includes themes of Genotyping and Selection.
His Single-nucleotide polymorphism research includes elements of Copy-number variation and Candidate gene.
He most often published in these fields:
- Genetics (58.97%)
- Genome-wide association study (31.41%)
- Computational biology (23.72%)
What were the highlights of his more recent work (between 2018-2021)?
- Computational biology (23.72%)
- Inference (10.26%)
- Artificial intelligence (8.33%)
In recent papers he was focusing on the following fields of study:
His scientific interests lie mostly in Computational biology, Inference, Artificial intelligence, Microbiome and Gene.
His research in Computational biology intersects with topics in Genome-wide association study, Sequence analysis and Allele.
His studies in Inference integrate themes in fields like Disease, Immune system, Bioinformatics and Gut microbiome.
His Artificial intelligence study which covers Machine learning that intersects with Indel, Classifier and Genome.
His Gene study is concerned with Genetics in general.
In his study, which falls under the umbrella issue of Genetics, Autism is strongly linked to Schizophrenia.
Between 2018 and 2021, his most popular works were:
- The Human Tumor Atlas Network: Charting Tumor Transitions Across Space and Time at Single-Cell Resolution (65 citations)
- FEAST: fast expectation-maximization for microbial source tracking. (50 citations)
- arcasHLA: high-resolution HLA typing from RNAseq. (19 citations)
In his most recent research, the most cited papers focused on:
Itsik Pe'er spends much of his time researching Genotype, Data mining, Computational biology, Gene and Artificial intelligence.
His Genotype research is multidisciplinary, relying on both Event, Ancient DNA, Inference and Population structure.
His Data mining research is multidisciplinary, incorporating perspectives in Microbiome and Gastrointestinal Microbiome.
While working in this field, Itsik Pe'er studies both Computational biology and High resolution.
His work carried out in the field of Gene brings together such families of science as Machine learning, Pathogen and Antibiotics, Antibiotic resistance.
In the field of Artificial intelligence, his study on Deep learning overlaps with subjects such as CASP, Multiple sequence alignment and Test data.
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.
