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Big Data in Omics and Imaging - Association Analysis (Hardcover)
Loot Price: R3,782
Discovery Miles 37 820
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Big Data in Omics and Imaging - Association Analysis (Hardcover)
Series: Chapman & Hall/CRC Computational Biology Series
Expected to ship within 12 - 17 working days
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Total price: R3,802
Discovery Miles: 38 020
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Big Data in Omics and Imaging: Association Analysis addresses the
recent development of association analysis and machine learning for
both population and family genomic data in sequencing era. It is
unique in that it presents both hypothesis testing and a data
mining approach to holistically dissecting the genetic structure of
complex traits and to designing efficient strategies for precision
medicine. The general frameworks for association analysis and
machine learning, developed in the text, can be applied to genomic,
epigenomic and imaging data. FEATURES Bridges the gap between the
traditional statistical methods and computational tools for small
genetic and epigenetic data analysis and the modern advanced
statistical methods for big data Provides tools for high
dimensional data reduction Discusses searching algorithms for model
and variable selection including randomization algorithms, Proximal
methods and matrix subset selection Provides real-world examples
and case studies Will have an accompanying website with R code The
book is designed for graduate students and researchers in genomics,
bioinformatics, and data science. It represents the paradigm shift
of genetic studies of complex diseases- from shallow to deep
genomic analysis, from low-dimensional to high dimensional,
multivariate to functional data analysis with next-generation
sequencing (NGS) data, and from homogeneous populations to
heterogeneous population and pedigree data analysis. Topics covered
are: advanced matrix theory, convex optimization algorithms,
generalized low rank models, functional data analysis techniques,
deep learning principle and machine learning methods for modern
association, interaction, pathway and network analysis of rare and
common variants, biomarker identification, disease risk and drug
response prediction.
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