2011 - Fellow of the American Association for the Advancement of Science (AAAS)
His primary areas of study are Genetics, Epistasis, Multifactor dimensionality reduction, Computational biology and Genetic association. His research on Epistasis also deals with topics like
His Machine learning research focuses on subjects like Data science, which are linked to Artificial intelligence. His work deals with themes such as Regulation of gene expression and Disease, which intersect with Computational biology. His Genetic association study combines topics from a wide range of disciplines, such as Genome-wide association study, Genetic variation and Genetic testing.
Artificial intelligence, Machine learning, Genetics, Computational biology and Epistasis are his primary areas of study. His Artificial intelligence research integrates issues from Data mining and Human genetics. Single-nucleotide polymorphism, Gene, Genome-wide association study, Allele and Genetic variation are among the areas of Genetics where he concentrates his study.
The study of Genome-wide association study is intertwined with the study of Genetic association in a number of ways. Jason H. Moore combines subjects such as Bioinformatics, Robustness, Phenotype, Genome and Disease with his study of Computational biology. His research integrates issues of Multifactor dimensionality reduction and Genetic architecture in his study of Epistasis.
His scientific interests lie mostly in Artificial intelligence, Machine learning, Genetic programming, Data science and Computational biology. His Artificial intelligence study integrates concerns from other disciplines, such as Pattern recognition and Big data. The various areas that Jason H. Moore examines in his Machine learning study include Tree, Context and Pipeline.
The Genetic programming study combines topics in areas such as Evolutionary computation, Set, Crossover and Benchmark. His Software research extends to Data science, which is thematically connected. His Computational biology research is multidisciplinary, incorporating elements of Phenotype, Gene, Genome-wide association study and Disease.
His scientific interests lie mostly in Artificial intelligence, Machine learning, Genetic programming, Data mining and Data science. His Artificial intelligence research incorporates elements of Generalization, Health records and Pipeline. His biological study spans a wide range of topics, including Tree, Stochastic optimization and Variable.
The concepts of his Data mining study are interwoven with issues in Bioconductor, Sample size determination and Missing data. His Single-nucleotide polymorphism study focuses on Gene and Genetics. Epistasis is closely connected to Computational biology in his research, which is encompassed under the umbrella topic of Enhancer.
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Multifactor-Dimensionality Reduction Reveals High-Order Interactions among Estrogen-Metabolism Genes in Sporadic Breast Cancer
Marylyn D. Ritchie;Lance W. Hahn;Nady Roodi;L. Renee Bailey.
American Journal of Human Genetics (2001)
Missing heritability and strategies for finding the underlying causes of complex disease
Evan E. Eichler;Jonathan Flint;Greg Gibson;Augustine Kong.
Nature Reviews Genetics (2010)
The Genetic Structure and History of Africans and African Americans
Sarah A. Tishkoff;Floyd A. Reed;Françoise R. Friedlaender;Christopher Ehret.
Multifactor dimensionality reduction software for detecting gene-gene and gene-environment interactions.
Lance W. Hahn;Marylyn D. Ritchie;Jason H. Moore.
Chapter 11: Genome-wide association studies.
William S. Bush;Jason H. Moore.
PLOS Computational Biology (2012)
Characterization of MicroRNA Expression Levels and Their Biological Correlates in Human Cancer Cell Lines
Arti Gaur;David A. Jewell;Yu Liang;Dana Ridzon.
Cancer Research (2007)
The Ubiquitous Nature of Epistasis in Determining Susceptibility to Common Human Diseases
Jason H. Moore.
Human Heredity (2003)
Proteomic patterns of tumour subsets in non-small-cell lung cancer.
Kiyoshi Yanagisawa;Yu Shyr;Baogang J Xu;Pierre P Massion.
The Lancet (2003)
A flexible computational framework for detecting, characterizing, and interpreting statistical patterns of epistasis in genetic studies of human disease susceptibility
Jason H. Moore;Joshua C. Gilbert;Chia-Ti Tsai;Fu-Tien Chiang.
Journal of Theoretical Biology (2006)
Power of multifactor dimensionality reduction for detecting gene‐gene interactions in the presence of genotyping error, missing data, phenocopy, and genetic heterogeneity
Marylyn D. Ritchie;Lance W. Hahn;Jason H. Moore.
Genetic Epidemiology (2003)
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