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Big Data in Omics and Imaging

Association Analysis
ISBN: 978-1-4987-2578-1
GTIN: 9781498725781
Einband: Fester Einband
Verfügbarkeit: Lieferbar in ca. 10-20 Arbeitstagen
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The text provides unified frameworks, basic knowledge and efficient computational tools for analyzing growing large, complex and diverse genomic, epigenomic, physiological and image data. It introduces currently developed statistical methods and software for big genomic and epigenomic data analysis with real-world examples and case studies."This is a fantastic book intensively focusing on the mathematical underpinnings of modern genome-wide association studies (GWAS). It serves well for senior graduate students in applied mathematics, computer science, and statistics who are interested in building a solid mathematical understanding of GWAS. Backgrounds of advanced mathematics and genetics are expected. It can also be used as a handbook for professionals to quickly check mathematical contexts of GWAS approaches and tools. This book is especially helpful for the latest generation of statistical geneticists who are pursuing academic career paths."~Journal of the American Statistical Association, Jing Su (Wake Forest School of Medicine)
The text provides unified frameworks, basic knowledge and efficient computational tools for analyzing growing large, complex and diverse genomic, epigenomic, physiological and image data. It introduces currently developed statistical methods and software for big genomic and epigenomic data analysis with real-world examples and case studies."This is a fantastic book intensively focusing on the mathematical underpinnings of modern genome-wide association studies (GWAS). It serves well for senior graduate students in applied mathematics, computer science, and statistics who are interested in building a solid mathematical understanding of GWAS. Backgrounds of advanced mathematics and genetics are expected. It can also be used as a handbook for professionals to quickly check mathematical contexts of GWAS approaches and tools. This book is especially helpful for the latest generation of statistical geneticists who are pursuing academic career paths."~Journal of the American Statistical Association, Jing Su (Wake Forest School of Medicine)
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AutorXiong, Momiao
VerlagTaylor and Francis
EinbandFester Einband
Erscheinungsjahr2017
Seitenangabe668 S.
AusgabekennzeichenEnglisch
AbbildungenFarb., s/w. Abb.
MasseH25.4 cm x B17.8 cm 1'678 g
CoverlagChapman and Hall/CRC (Imprint/Brand)
Auflage1. A.
ReiheChapman & Hall/CRC Computational Biology Series
Gewicht1678
ISBN978-1-4987-2578-1

Über den Autor Momiao Xiong

Momiao Xiong, is a professor in the Department of Biostatistics and Data Science, University of Texas School of Public Health, and a regular member in the Genetics & Epigenetics (G&E) Graduate Program at The University of Texas MD Anderson Cancer Center, UTHealth Graduate School of Biomedical Science. His interests are artificial intelligence, causal inference, bioinformatics and genomics.

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