2019 - Fellow of the International Society for Computational Biology
2009 - Fellow of Alfred P. Sloan Foundation
The scientist’s investigation covers issues in Genetics, Genome-wide association study, Quantitative trait locus, Artificial intelligence and Genetic association. Genetic variation, Gene, Inbred strain, Linkage disequilibrium and Genome are among the areas of Genetics where Eleazar Eskin concentrates his study. His Genome-wide association study study integrates concerns from other disciplines, such as Expression quantitative trait loci, Pairwise comparison, Heritability, Computational biology and Candidate gene.
Eleazar Eskin combines subjects such as Spurious relationship and Statistical hypothesis testing with his study of Quantitative trait locus. His Artificial intelligence research includes elements of Machine learning, Information retrieval and Pattern recognition. His biological study spans a wide range of topics, including False positive paradox and Principal component analysis.
Eleazar Eskin focuses on Genetics, Computational biology, Genome-wide association study, Genetic association and Gene. Quantitative trait locus, Single-nucleotide polymorphism, Genome, Expression quantitative trait loci and Haplotype are among the areas of Genetics where the researcher is concentrating his efforts. He has researched Single-nucleotide polymorphism in several fields, including Genetic variation and Allele frequency.
His Computational biology research incorporates elements of RNA, Microbiome, Bioinformatics and Genomics. His Genome-wide association study research is multidisciplinary, relying on both Meta-analysis, Locus, Imputation, Multivariate normal distribution and Candidate gene. The Genetic association study combines topics in areas such as Association mapping, Linkage disequilibrium, Inbred strain and Statistical power.
His scientific interests lie mostly in Computational biology, Genome-wide association study, Genetic association, Genetics and Gene. His Computational biology research is multidisciplinary, incorporating perspectives in Microbiome, DNA methylation, RNA, Genomics and Epigenetics. His work carried out in the field of Genome-wide association study brings together such families of science as Phenotype, Statistical power, Quantitative trait locus, Imputation and Multivariate normal distribution.
His Quantitative trait locus study combines topics in areas such as Expression quantitative trait loci, Genetic variation and Heritability. His Genetic association study also includes fields such as
Eleazar Eskin mainly investigates Genetics, Genome-wide association study, Computational biology, Gene and Data science. His Genome-wide association study research integrates issues from Quantitative trait locus, Linkage disequilibrium and Functional genomics, Genomics. His studies deal with areas such as Expression quantitative trait loci, Genetic variation and Heritability as well as Quantitative trait locus.
His Linkage disequilibrium study deals with Genetic Pleiotropy intersecting with Data mining. His Computational biology research incorporates elements of Methylation, DNA methylation, RNA, Genetic association and Epigenetics. Eleazar Eskin interconnects Locus and Genetic architecture in the investigation of issues within Genetic association.
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.
Variance component model to account for sample structure in genome-wide association studies
Hyun Min Kang;Jae Hoon Sul;Noah A. Zaitlen.
Nature Genetics (2010)
Efficient Control of Population Structure in Model Organism Association Mapping
Hyun Min Kang;Noah A. Zaitlen;Claire M. Wade;Claire M. Wade;Andrew Kirby;Andrew Kirby.
Genetics (2008)
Assessing computational tools for the discovery of transcription factor binding sites.
Martin Tompa;Nan Li;Timothy L. Bailey;George M. Church.
Nature Biotechnology (2005)
Mouse genomic variation and its effect on phenotypes and gene regulation
T M Keane;L Goodstadt;P Danecek;M A White.
Nature (2011)
Whole-Genome Patterns of Common DNA Variation in Three Human Populations
David A. Hinds;David A. Hinds;Laura L. Stuve;Laura L. Stuve;Geoffrey B. Nilsen;Geoffrey B. Nilsen;Eran Halperin;Eran Halperin.
Science (2005)
A Geometric Framework for Unsupervised Anomaly Detection
Eleazar Eskin;Andrew Arnold;Michael J. Prerau;Leonid Portnoy.
Applications of Data Mining in Computer Security (2002)
The spectrum kernel: a string kernel for SVM protein classification.
Christina S. Leslie;Eleazar Eskin;William Stafford Noble.
pacific symposium on biocomputing (2001)
Data mining methods for detection of new malicious executables
M.G. Schultz;E. Eskin;F. Zadok;S.J. Stolfo.
ieee symposium on security and privacy (2001)
A GEOMETRIC FRAMEWORK FOR UNSUPERVISED ANOMALY DETECTION: DETECTING INTRUSIONS IN UNLABELED DATA
E Eskin;A Arnold;M Prerau.
Applications of Data Mining in Computer Security (2002)
Mismatch string kernels for discriminative protein classification
Christina S. Leslie;Eleazar Eskin;Adiel Cohen;Jason Weston.
Bioinformatics (2004)
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