2009 - Member of the National Academy of Medicine (NAM)
2006 - Fellow of the Indian National Academy of Engineering (INAE)
His primary areas of investigation include Informatics, Genetics, Bioinformatics, Health care and Gene expression profiling. The study incorporates disciplines such as Chart, Medical record, Health informatics, World Wide Web and Data science in addition to Informatics. His study focuses on the intersection of Genetics and fields such as Computational biology with connections in the field of Genome, Functional genomics and Microarray analysis techniques.
Isaac S. Kohane interconnects Major depressive disorder, Genetic testing, Disease and DNA sequencing in the investigation of issues within Bioinformatics. He usually deals with Major depressive disorder and limits it to topics linked to Mendelian randomization and Case-control study and Genome-wide association study. His Gene expression profiling study combines topics in areas such as Regulation of gene expression and Inclusion body myositis.
Isaac S. Kohane mainly investigates Internal medicine, Genetics, Gene, Bioinformatics and Disease. His work carried out in the field of Internal medicine brings together such families of science as Gastroenterology and Endocrinology. His Genetics study often links to related topics such as Computational biology.
His work in Gene expression and Gene expression profiling are all subfields of Gene research. His specific area of interest is Disease, where Isaac S. Kohane studies Ulcerative colitis. His study looks at the intersection of Ulcerative colitis and topics like Crohn's disease with Surgery.
The scientist’s investigation covers issues in Artificial intelligence, Health care, MEDLINE, Machine learning and Computational biology. His work on Deep learning, Artificial neural network and Benchmark as part of general Artificial intelligence research is frequently linked to Set, thereby connecting diverse disciplines of science. His research in Health care focuses on subjects like Retrospective cohort study, which are connected to Observational study.
His Computational biology study typically links adjacent topics like Gene expression. His Gene expression research is classified as research in Gene. His Phenotype study deals with the bigger picture of Genetics.
Isaac S. Kohane mainly focuses on Artificial intelligence, Health care, MEDLINE, Genetics and Retrospective cohort study. His Artificial intelligence research includes elements of Machine learning and Natural language processing. His research integrates issues of Artificial neural network, Health informatics and Sex factors in his study of MEDLINE.
His research in Health informatics intersects with topics in World Wide Web, Workflow and Data visualization. His study in Retrospective cohort study is interdisciplinary in nature, drawing from both Test, Observational study, Pharmacy, Confidence interval and Emergency medicine. Isaac S. Kohane studied Disease and Functional studies that intersect with Bioinformatics.
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.
Comprehensive genomic characterization defines human glioblastoma genes and core pathways
Roger McLendon;Allan Friedman;Darrell Bigner;Erwin G. Van Meir.
Coordinated reduction of genes of oxidative metabolism in humans with insulin resistance and diabetes: Potential role of PGC1 and NRF1
Mary Elizabeth Patti;Atul J. Butte;Sarah Crunkhorn;Kenneth Cusi.
Proceedings of the National Academy of Sciences of the United States of America (2003)
Gene regulation and DNA damage in the ageing human brain.
Tao Lu;Ying Pan;Shyan Yuan Kao;Cheng Li.
Mutual information relevance networks: functional genomic clustering using pairwise entropy measurements.
A J Butte;I S Kohane.
pacific symposium on biocomputing (1999)
A mega-analysis of genome-wide association studies for major depressive disorder
Stephan Ripke;Naomi R Wray;Cathryn M Lewis;Steven P Hamilton.
Molecular Psychiatry (2013)
A signature of chromosomal instability inferred from gene expression profiles predicts clinical outcome in multiple human cancers
Scott L Carter;Aron C Eklund;Aron C Eklund;Isaac S Kohane;Lyndsay N Harris.
Nature Genetics (2006)
Genome-wide association analyses identify 44 risk variants and refine the genetic architecture of major depression
Naomi R. Wray;Stephan Ripke;Stephan Ripke;Stephan Ripke;Manuel Mattheisen;MacIej Trzaskowski.
Nature Genetics (2018)
Serving the enterprise and beyond with informatics for integrating biology and the bedside (i2b2)
Shawn N. Murphy;Shawn N. Murphy;Griffin M. Weber;Griffin M. Weber;Michael Mendis;Vivian S. Gainer.
Journal of the American Medical Informatics Association (2010)
Discovering statistically significant pathways in expression profiling studies
Lu Tian;Steven A. Greenberg;Sek Won Kong;Josiah Altschuler.
Proceedings of the National Academy of Sciences of the United States of America (2005)
Discovering functional relationships between RNA expression and chemotherapeutic susceptibility using relevance networks
Atul J. Butte;Pablo Tamayo;Donna Slonim;Todd R. Golub.
Proceedings of the National Academy of Sciences of the United States of America (2000)
Profile was last updated on December 6th, 2021.
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