Analyzing High-Dimensional Gene Expression and DNA Methylation Data with R - Info and Reading Options
By Hongmei Zhang
"Analyzing High-Dimensional Gene Expression and DNA Methylation Data with R" was published by Taylor & Francis Group in 2020 - Boca Raton, it has 200 pages and the language of the book is English.
“Analyzing High-Dimensional Gene Expression and DNA Methylation Data with R” Metadata:
- Title: ➤ Analyzing High-Dimensional Gene Expression and DNA Methylation Data with R
- Author: Hongmei Zhang
- Language: English
- Number of Pages: 200
- Publisher: Taylor & Francis Group
- Publish Date: 2020
- Publish Location: Boca Raton
“Analyzing High-Dimensional Gene Expression and DNA Methylation Data with R” Subjects and Themes:
- Subjects: ➤ Biology - Gene expression - Statistical methods - Data processing - DNA - Methylation - Epigenetics - R (Computer program language) - Expression génique - Méthodes statistiques - Informatique - ADN - Méthylation - Épigénétique - R (Langage de programmation) - SCIENCE / Biotechnology - SCIENCE / Life Sciences / Biology / General
Edition Identifiers:
- The Open Library ID: OL28076865M - OL20752918W
- Online Computer Library Center (OCLC) ID: 1155638000
- Library of Congress Control Number (LCCN): 2020006653
- ISBN-13: 9781498772594 - 9780429155192
- All ISBNs: 9781498772594 - 9780429155192
AI-generated Review of “Analyzing High-Dimensional Gene Expression and DNA Methylation Data with R”:
"Analyzing High-Dimensional Gene Expression and DNA Methylation Data with R" Description:
Open Data:
"This will be the first book to systematically describe the process and provide corresponding methods for analysing data generated from genetic- and epigenetic-studies. The overall subject is on large data analysis. Specifically, the book aims to provide apipe line for genetic and epigenetic data analysis starting from raw genome- and epigenome-scale data. It includes methods to pre-process genetic and epigenetic data in the genome-scale, methods for data mining to identify potentially informative factors, and methods for subsequent analyses after data mining, e.g., factor/variable selections, network construction, and testing for differential networks"--
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