Overview
- Serves as a guide to biological interpretation of complex data
- Written with experimental and data-oriented scientists in mind, it presents both the quantitative and qualitative aspects of transcriptome analysis
- Spans topics from basic methods, like plotting data, to more advanced ones, such as network analysis
- Provides extensive applications and biological interpretation of transcriptome analysis
Part of the book series: Publications of the Scuola Normale Superiore (PSNS, volume 17)
Part of the book sub series: Lecture Notes (Scuola Normale Superiore) (LNSNS)
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Table of contents (9 chapters)
Keywords
About this book
Undergraduate and graduate students in biomedical and quantitative sciences will benefit from this text as well as academics untrained in the subject.
Authors and Affiliations
About the authors
Alessandro Cellerino's scientific interests relate to neuroscience, biology of aging and functional genomics. He has published a monograph and numerous articles within these fields and he teaches neurogenomics and biology of aging.
Michele Sanguanini is a member of Gonville& Caius College, a PhD candidate at the Centre for Misfolding Diseases (Department of Chemistry, University of Cambridge), and a Scuola Normale Superiore graduate. His research involves the systems biology and biophysics underlying neurodegenerative processes and ageing, with a focus on protein aggregation in Alzheimer’s disease.
Bibliographic Information
Book Title: Transcriptome Analysis
Book Subtitle: Introduction and Examples from the Neurosciences
Authors: Alessandro Cellerino, Michele Sanguanini
Series Title: Publications of the Scuola Normale Superiore
DOI: https://doi.org/10.1007/978-88-7642-642-1
Publisher: Edizioni della Normale Pisa
eBook Packages: Mathematics and Statistics, Mathematics and Statistics (R0)
Copyright Information: Scuola Normale Superiore Pisa 2018
Softcover ISBN: 978-88-7642-641-4Due: 06 March 2019
eBook ISBN: 978-88-7642-642-1Published: 14 June 2018
Series ISSN: 2239-1460
Series E-ISSN: 2532-1668
Edition Number: 1
Number of Pages: XIV, 188
Topics: Genetics and Population Dynamics, Computational Biology/Bioinformatics, Systems Biology