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Computational and statistical epigen...

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Computational and statistical epigenomics[electronic resource] /

紀錄類型:	書目-電子資源 : Monograph/item
杜威分類號:	616.042
書名/作者:	Computational and statistical epigenomics/ edited by Andrew E. Teschendorff.
其他作者:	Teschendorff, Andrew E.
出版者:	Dordrecht : : Springer Netherlands :, 2015.
面頁冊數:	v, 217 p. : : ill., digital ;; 24 cm.
Contained By:	Springer eBooks
標題:	Epigenetics - Mathematical models.
標題:	Life Sciences.
標題:	Bioinformatics.
標題:	Computer Appl. in Life Sciences.
標題:	Molecular Medicine.
標題:	Epidemiology.
ISBN:	9789401799270 (electronic bk.)
ISBN:	9789401799263 (paper)
摘要、提要註:	This book introduces the reader to modern computational and statistical tools for translational epigenomics research. Over the last decade, epigenomics has emerged as a key area of molecular biology, epidemiology and genome medicine. Epigenomics not only offers us a deeper understanding of fundamental cellular biology, but also provides us with the basis for an improved understanding and management of complex diseases. From novel biomarkers for risk prediction, early detection, diagnosis and prognosis of common diseases, to novel therapeutic strategies, epigenomics is set to play a key role in the personalized medicine of the future. In this book we introduce the reader to some of the most important computational and statistical methods for analyzing epigenomic data, with a special focus on DNA methylation. Topics include normalization, correction for cellular heterogeneity, batch effects, clustering, supervised analysis and integrative methods for systems epigenomics. This book will be of interest to students and researchers in bioinformatics, biostatistics, biologists and clinicians alike. Dr. Andrew E. Teschendorff is Head of the Computational Systems Genomics Lab at the CAS-MPG Partner Institute for Computational Biology, Shanghai, China, as well as an Honorary Research Fellow at the UCL Cancer Institute, University College London, UK.
電子資源:	http://dx.doi.org/10.1007/978-94-017-9927-0

Computational and statistical epigenomics[electronic resource] /
Computational and statistical epigenomics[electronic resource] /edited by Andrew E. Teschendorff. - Dordrecht :Springer Netherlands :2015. - v, 217 p. :ill., digital ;24 cm. - Translational bioinformatics,v.72213-2775 ;. - Translational bioinformatics ;v.2.

This book introduces the reader to modern computational and statistical tools for translational epigenomics research. Over the last decade, epigenomics has emerged as a key area of molecular biology, epidemiology and genome medicine. Epigenomics not only offers us a deeper understanding of fundamental cellular biology, but also provides us with the basis for an improved understanding and management of complex diseases. From novel biomarkers for risk prediction, early detection, diagnosis and prognosis of common diseases, to novel therapeutic strategies, epigenomics is set to play a key role in the personalized medicine of the future. In this book we introduce the reader to some of the most important computational and statistical methods for analyzing epigenomic data, with a special focus on DNA methylation. Topics include normalization, correction for cellular heterogeneity, batch effects, clustering, supervised analysis and integrative methods for systems epigenomics. This book will be of interest to students and researchers in bioinformatics, biostatistics, biologists and clinicians alike. Dr. Andrew E. Teschendorff is Head of the Computational Systems Genomics Lab at the CAS-MPG Partner Institute for Computational Biology, Shanghai, China, as well as an Honorary Research Fellow at the UCL Cancer Institute, University College London, UK.

ISBN: 9789401799270 (electronic bk.)

Standard No.: 10.1007/978-94-017-9927-0doiSubjects--Topical Terms:

626139
Epigenetics
--Mathematical models.

LC Class. No.: RB155

Dewey Class. No.: 616.042

Computational and statistical epigenomics[electronic resource] /
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520 $a This book introduces the reader to modern computational and statistical tools for translational epigenomics research. Over the last decade, epigenomics has emerged as a key area of molecular biology, epidemiology and genome medicine. Epigenomics not only offers us a deeper understanding of fundamental cellular biology, but also provides us with the basis for an improved understanding and management of complex diseases. From novel biomarkers for risk prediction, early detection, diagnosis and prognosis of common diseases, to novel therapeutic strategies, epigenomics is set to play a key role in the personalized medicine of the future. In this book we introduce the reader to some of the most important computational and statistical methods for analyzing epigenomic data, with a special focus on DNA methylation. Topics include normalization, correction for cellular heterogeneity, batch effects, clustering, supervised analysis and integrative methods for systems epigenomics. This book will be of interest to students and researchers in bioinformatics, biostatistics, biologists and clinicians alike. Dr. Andrew E. Teschendorff is Head of the Computational Systems Genomics Lab at the CAS-MPG Partner Institute for Computational Biology, Shanghai, China, as well as an Honorary Research Fellow at the UCL Cancer Institute, University College London, UK.
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