{"product_id":"big-data-in-omics-and-imaging-two-volume-set-chapman-hall-crc-computational-biology-1st-ed","title":"Big Data in Omics and Imaging, Two Volume Set (Chapman \u0026 Hall\/CRC Computational Biology) (1ST ed.)","description":"\n\u003ctable align=\"center\" border=\"0\" cellpadding=\"2\" cellspacing=\"0\" width=\"100%\"\u003e\n\u003ctr\u003e\n\u003ctd class=\"productDetailSmallElements\"\u003e\n\u003cp\u003e\n\u003cstrong\u003eTable of Contents\u003c\/strong\u003e:\u003cbr\u003e\n\u003c\/p\u003e\n\u003cp\u003eK25794: \u003c\/p\u003e\n\u003cp\u003eMathematical Foundation. \u003c\/p\u003e\n\u003cp\u003eLinkage Disequilibrium. \u003c\/p\u003e\n\u003cp\u003eAssociation Studies for Qualitative Traits. \u003c\/p\u003e\n\u003cp\u003eAssociation Studies for Quantitative Traits. \u003c\/p\u003e\n\u003cp\u003eMultiple Phenotype Association Studies.\u003c\/p\u003e\n\u003cp\u003eK345128\u003c\/p\u003e\n\u003cp\u003ePreface\u003c\/p\u003e\n\u003cp\u003eAuthor\u003c\/p\u003e\n\u003cp\u003e1. Genotype-Phenotype Network Analysis\u003c\/p\u003e\n\u003cp\u003e2. Causal Analysis and Network Biology \u003c\/p\u003e\n\u003cp\u003e3. Wearable Computing and Genetic Analysis of Function-Valued Traits \u003c\/p\u003e\n\u003cp\u003e4. RNA-Seq Data Analysis \u003c\/p\u003e\n\u003cp\u003e5. Methylation Data Analysis \u003c\/p\u003e\n\u003cp\u003e6. Imaging and Genomics \u003c\/p\u003e\n\u003cp\u003e7. From Association Analysis to Integrated Causal Inference \u003c\/p\u003e\n\u003cp\u003eReferences\u003c\/p\u003e\n\u003cp\u003eIndex\u003c\/p\u003e\n\u003cbr\u003e\u003cbr\u003e\n\u003cstrong\u003eBiographical Note\u003c\/strong\u003e:\u003cbr\u003e\n\u003cp\u003e\u003cstrong\u003eMomiao Xiong\u003c\/strong\u003e is a professor of Biostatistics at the University of Texas Health Science Center in Houston where he has worked since 1997. He received his PhD in 1993 from the University of Georgia.\u003c\/p\u003e\n\u003cbr\u003e\u003cbr\u003e\n\u003cstrong\u003ePublisher Marketing\u003c\/strong\u003e:\u003cbr\u003e\n\u003cp\u003e\u003cb\u003eFEATURES\u003c\/b\u003e\u003c\/p\u003e\n\u003cp\u003eBridges 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\u003c\/p\u003e\n\u003cp\u003eProvides tools for high dimensional data reduction\u003c\/p\u003e\n\u003cp\u003eDiscusses searching algorithms for model and variable selection including randomization algorithms, Proximal methods and matrix subset selection\u003c\/p\u003e\n\u003cp\u003eProvides real-world examples and case studies\u003c\/p\u003e\n\u003cp\u003eWill have an accompanying website with R code\u003c\/p\u003e\n\u003cp\u003eProvides a natural extension and companion volume to \u003ci\u003eBig Data in Omic and Imaging: Association Analysis, \u003c\/i\u003e but can be read independently. \u003c\/p\u003e\n\u003cp\u003eIntroduce causal inference theory to genomic, epigenomic and imaging data analysis \u003c\/p\u003e\n\u003cp\u003eDevelop novel statistics for genome-wide causation studies and epigenome-wide causation studies. \u003c\/p\u003e\n\u003cp\u003eBridge the gap between the traditional association analysis and modern causation analysis \u003c\/p\u003e\n\u003cp\u003eUse combinatorial optimization methods and various causal models as a general framework for inferring multilevel omic and image causal networks \u003c\/p\u003e\n\u003cp\u003ePresent statistical methods and computational algorithms for searching causal paths from genetic variant to disease \u003c\/p\u003e\n\u003cp\u003eDevelop causal machine learning methods integrating causal inference and machine learning \u003c\/p\u003e\n\u003cp\u003eDevelop statistics for testing significant difference in directed edge, path, and graphs, and for assessing causal relationships between two networks \u003c\/p\u003e\n\u003cp\u003e\u003c\/p\u003eThe 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. \n\u003cp\u003e\u003c\/p\u003e\n\u003cbr\u003e\u003cbr\u003e\n\n\u003cbr\u003e\n\u003cbr\u003e\n\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003c\/table\u003e\n","brand":"CRC Press","offers":[{"title":"Default Title","offer_id":47448903155843,"sku":"9780367002183","price":432.0,"currency_code":"USD","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0564\/6830\/8099\/files\/9780367002183.jpg?v=1783318992","url":"https:\/\/sebink.com\/products\/big-data-in-omics-and-imaging-two-volume-set-chapman-hall-crc-computational-biology-1st-ed","provider":"Sebink","version":"1.0","type":"link"}