9780815387107 - big data in omics and imaging: integrated analysis and causal inference (chapman & hall/crc computational biology series) de xiong, momiao (13 resultados)
Idioma: Inglés
Editorial: Taylor & Francis Inc, Bosa Roca, 2018
Serie: Chapman & Hall/CRC Computational Biology Series, Libro 4 de 50. Libro 4 de 50 - Chapman & Hall/CRC Computational Biology Series
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Librería: Grand Eagle Retail, Bensenville, IL, Estados Unidos de AmericaGrand Eagle Retail
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EUR 170,75
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Hardcover. Condición: new. Hardcover. Big Data in Omics and Imaging: Integrated Analysis and Causal Inference addresses the recent development of integrated genomic, epigenomic and imaging data analysis and causal inference in big data era. Despite significant progress in dissecting the genetic architecture of complex diseases b…y genome-wide association studies (GWAS), genome-wide expression studies (GWES), and epigenome-wide association studies (EWAS), the overall contribution of the new identified genetic variants is small and a large fraction of genetic variants is still hidden. Understanding the etiology and causal chain of mechanism underlying complex diseases remains elusive. It is time to bring big data, machine learning and causal revolution to developing a new generation of genetic analysis for shifting the current paradigm of genetic analysis from shallow association analysis to deep causal inference and from genetic analysis alone to integrated omics and imaging data analysis for unraveling the mechanism of complex diseases.FEATURES Provides a natural extension and companion volume to Big Data in Omic and Imaging: Association Analysis, but can be read independently. Introduce causal inference theory to genomic, epigenomic and imaging data analysis Develop novel statistics for genome-wide causation studies and epigenome-wide causation studies. Bridge the gap between the traditional association analysis and modern causation analysis Use combinatorial optimization methods and various causal models as a general framework for inferring multilevel omic and image causal networks Present statistical methods and computational algorithms for searching causal paths from genetic variant to disease Develop causal machine learning methods integrating causal inference and machine learning Develop statistics for testing significant difference in directed edge, path, and graphs, and for assessing causal relationships between two networksThe book is designed for graduate students and researchers in genomics, epigenomics, medical image, bioinformatics, and data science. Topics covered are: mathematical formulation of causal inference, information geometry for causal inference, topology group and Haar measure, additive noise models, distance correlation, multivariate causal inference and causal networks, dynamic causal networks, multivariate and functional structural equation models, mixed structural equation models, causal inference with confounders, integer programming, deep learning and differential equations for wearable computing, genetic analysis of function-valued traits, RNA-seq data analysis, causal networks for genetic methylation analysis, gene expression and methylation deconvolution, cell specific causal networks, deep learning for image segmentation and image analysis, imaging and genomic data analysis, integrated multilevel causal genomic, epigenomic and imaging data analysis. Emerging genomic, epigenomic, sensing and image technologies will produce massive, dimensional genomic, epigenomic, physiological, image and clinical data. The book is designed to introduce the currently developed statistical methods and software for big genomic and epigenomic data analysis. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.
Idioma: Inglés
Editorial: Chapman and Hall/CRC, 2018
Serie: Chapman & Hall/CRC Computational Biology Series, Libro 4 de 50. Libro 4 de 50 - Chapman & Hall/CRC Computational Biology Series
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Librería: GreatBookPrices, Columbia, MD, Estados Unidos de AmericaGreatBookPrices
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Condición: New.
Idioma: Inglés
Editorial: Chapman and Hall/CRC, 2018
Serie: Chapman & Hall/CRC Computational Biology Series, Libro 4 de 50. Libro 4 de 50 - Chapman & Hall/CRC Computational Biology Series
- Tapa dura
Librería: GreatBookPrices, Columbia, MD, Estados Unidos de AmericaGreatBookPrices
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Condición: As New. Unread book in perfect condition.
Idioma: Inglés
Editorial: Chapman and Hall/CRC, 2018
Serie: Chapman & Hall/CRC Computational Biology Series, Libro 4 de 50. Libro 4 de 50 - Chapman & Hall/CRC Computational Biology Series
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Librería: GreatBookPricesUK, Woodford Green, Reino UnidoGreatBookPricesUK
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Condición: As New. Unread book in perfect condition.
Idioma: Inglés
Editorial: Chapman and Hall/CRC, 2018
Serie: Chapman & Hall/CRC Computational Biology Series, Libro 4 de 50. Libro 4 de 50 - Chapman & Hall/CRC Computational Biology Series
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Librería: Majestic Books, Hounslow, Reino UnidoMajestic Books
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Condición: New.
Idioma: Inglés
Editorial: Taylor & Francis Inc, Bosa Roca, 2018
Serie: Chapman & Hall/CRC Computational Biology Series, Libro 4 de 50. Libro 4 de 50 - Chapman & Hall/CRC Computational Biology Series
- Tapa dura
Librería: AussieBookSeller, Truganina, VIC, AustraliaAussieBookSeller
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Hardcover. Condición: new. Hardcover. Big Data in Omics and Imaging: Integrated Analysis and Causal Inference addresses the recent development of integrated genomic, epigenomic and imaging data analysis and causal inference in big data era. Despite significant progress in dissecting the genetic architecture of complex diseases b…y genome-wide association studies (GWAS), genome-wide expression studies (GWES), and epigenome-wide association studies (EWAS), the overall contribution of the new identified genetic variants is small and a large fraction of genetic variants is still hidden. Understanding the etiology and causal chain of mechanism underlying complex diseases remains elusive. It is time to bring big data, machine learning and causal revolution to developing a new generation of genetic analysis for shifting the current paradigm of genetic analysis from shallow association analysis to deep causal inference and from genetic analysis alone to integrated omics and imaging data analysis for unraveling the mechanism of complex diseases.FEATURES Provides a natural extension and companion volume to Big Data in Omic and Imaging: Association Analysis, but can be read independently. Introduce causal inference theory to genomic, epigenomic and imaging data analysis Develop novel statistics for genome-wide causation studies and epigenome-wide causation studies. Bridge the gap between the traditional association analysis and modern causation analysis Use combinatorial optimization methods and various causal models as a general framework for inferring multilevel omic and image causal networks Present statistical methods and computational algorithms for searching causal paths from genetic variant to disease Develop causal machine learning methods integrating causal inference and machine learning Develop statistics for testing significant difference in directed edge, path, and graphs, and for assessing causal relationships between two networksThe book is designed for graduate students and researchers in genomics, epigenomics, medical image, bioinformatics, and data science. Topics covered are: mathematical formulation of causal inference, information geometry for causal inference, topology group and Haar measure, additive noise models, distance correlation, multivariate causal inference and causal networks, dynamic causal networks, multivariate and functional structural equation models, mixed structural equation models, causal inference with confounders, integer programming, deep learning and differential equations for wearable computing, genetic analysis of function-valued traits, RNA-seq data analysis, causal networks for genetic methylation analysis, gene expression and methylation deconvolution, cell specific causal networks, deep learning for image segmentation and image analysis, imaging and genomic data analysis, integrated multilevel causal genomic, epigenomic and imaging data analysis. Emerging genomic, epigenomic, sensing and image technologies will produce massive, dimensional genomic, epigenomic, physiological, image and clinical data. The book is designed to introduce the currently developed statistical methods and software for big genomic and epigenomic data analysis. Shipping may be from our Sydney, NSW warehouse or from our UK or US warehouse, depending on stock availability.
Idioma: Inglés
Editorial: Chapman and Hall/CRC, 2018
Serie: Chapman & Hall/CRC Computational Biology Series, Libro 4 de 50. Libro 4 de 50 - Chapman & Hall/CRC Computational Biology Series
- Tapa dura
Librería: GreatBookPricesUK, Woodford Green, Reino UnidoGreatBookPricesUK
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Condición: New.
Idioma: Inglés
Editorial: Chapman and Hall/CRC, 2018
Serie: Chapman & Hall/CRC Computational Biology Series, Libro 4 de 50. Libro 4 de 50 - Chapman & Hall/CRC Computational Biology Series
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Librería: Books Puddle, New York, NY, Estados Unidos de AmericaBooks Puddle
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EUR 222,00
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Condición: New.
Idioma: Inglés
Editorial: CRC Pr I Llc, 2018
Serie: Chapman & Hall/CRC Computational Biology Series, Libro 4 de 50. Libro 4 de 50 - Chapman & Hall/CRC Computational Biology Series
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Librería: Revaluation Books, Exeter, Reino UnidoRevaluation Books
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Hardcover. Condición: Brand New. 736 pages. 9.50x6.50x2.00 inches. In Stock.
Idioma: Inglés
Editorial: CRC Press, 2018
Serie: Chapman & Hall/CRC Computational Biology Series, Libro 4 de 50. Libro 4 de 50 - Chapman & Hall/CRC Computational Biology Series
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Librería: moluna, Greven, Alemaniamoluna
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Condición: New. Momiao Xiong 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.Big Data in Omics .
Idioma: Inglés
Editorial: Chapman and Hall/CRC, 2018
Serie: Chapman & Hall/CRC Computational Biology Series, Libro 4 de 50. Libro 4 de 50 - Chapman & Hall/CRC Computational Biology Series
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Librería: Biblios, frankfurt am main, HESSE, AlemaniaBiblios
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Condición: New.
Idioma: Inglés
Editorial: CRC Press Jun 2018, 2018
Serie: Chapman & Hall/CRC Computational Biology Series, Libro 4 de 50. Libro 4 de 50 - Chapman & Hall/CRC Computational Biology Series
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Librería: AHA-BUCH GmbH, Einbeck, AlemaniaAHA-BUCH GmbH
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Buch. Condición: Neu. Neuware - Big Data in Omics and Imaging: Integrated Analysis and Causal Inference addresses the recent development of integrated genomic, epigenomic and imaging data analysis and causal inference in big data era. Despite significant progress in dissecting the genetic architecture of complex diseases by geno…me-wide association studies (GWAS), genome-wide expression studies (GWES), and epigenome-wide association studies (EWAS), the overall contribution of the new identified genetic variants is small and a large fraction of genetic variants is still hidden. Understanding the etiology and causal chain of mechanism underlying complex diseases remains elusive. It is time to bring big data, machine learning and causal revolution to developing a new generation of genetic analysis for shifting the current paradigm of genetic analysis from shallow association analysis to deep causal inference and from genetic analysis alone to integrated omics and imaging data analysis for unraveling the mechanism of complex diseases.FEATURES - Provides a natural extension and companion volume to Big Data in Omic and Imaging: Association Analysis, but can be read independently. - Introduce causal inference theory to genomic, epigenomic and imaging data analysis - Develop novel statistics for genome-wide causation studies and epigenome-wide causation studies. - Bridge the gap between the traditional association analysis and modern causation analysis - Use combinatorial optimization methods and various causal models as a general framework for inferring multilevel omic and image causal networks - Present statistical methods and computational algorithms for searching causal paths from genetic variant to disease - Develop causal machine learning methods integrating causal inference and machine learning - Develop statistics for testing significant difference in directed edge, path, and graphs, and for assessing causal relationships between two networks The book is designed for graduate students and researchers in genomics, epigenomics, medical image, bioinformatics, and data science. Topics covered are: mathematical formulation of causal inference, information geometry for causal inference, topology group and Haar measure, additive noise models, distance correlation, multivariate causal inference and causal networks, dynamic causal networks, multivariate and functional structural equation models, mixed structural equation models, causal inference with confounders, integer programming, deep learning and differential equations for wearable computing, genetic analysis of function-valued traits, RNA-seq data analysis, causal networks for genetic methylation analysis, gene expression and methylation deconvolution, cell -specific causal networks, deep learning for image segmentation and image analysis, imaging and genomic data analysis, integrated multilevel causal genomic, epigenomic and imaging data analysis.
Idioma: Inglés
Editorial: Chapman and Hall/CRC, 2018
Serie: Chapman & Hall/CRC Computational Biology Series, Libro 4 de 50. Libro 4 de 50 - Chapman & Hall/CRC Computational Biology Series
- Tapa dura
Librería: Mispah books, Redhill, SURRE, Reino UnidoMispah books
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EUR 279,03
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Hardcover. Condición: New. NEW. SHIPS FROM MULTIPLE LOCATIONS. book.
