Overview
- Includes supplementary material: sn.pub/extras
Part of the book series: SpringerBriefs in Electrical and Computer Engineering (BRIEFSELECTRIC)
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Table of contents (5 chapters)
Keywords
About this book
Inferring gene regulatory networks is a difficult problem to solve due to the relative scarcity of data compared to the potential size of the networks. While researchers have developed techniques to find some of the underlying network structure, there is still no one-size-fits-all algorithm for every data set.
Network Inference in Molecular Biology examines the current techniques used by researchers, and provides key insights into which algorithms best fit a collection of data. Through a series of in-depth examples, the book also outlines how to mix-and-match algorithms, in order to create one tailored to a specific data situation.
Network Inference in Molecular Biology is intended for advanced-level students and researchers as a reference guide. Practitioners and professionals working in a related field will also find this book valuable.
Authors and Affiliations
Bibliographic Information
Book Title: Network Inference in Molecular Biology
Book Subtitle: A Hands-on Framework
Authors: Jesse M. Lingeman, Dennis Shasha
Series Title: SpringerBriefs in Electrical and Computer Engineering
DOI: https://doi.org/10.1007/978-1-4614-3113-8
Publisher: Springer New York, NY
eBook Packages: Engineering, Engineering (R0)
Copyright Information: The Author(s) 2012
Softcover ISBN: 978-1-4614-3112-1Published: 25 May 2012
eBook ISBN: 978-1-4614-3113-8Published: 24 May 2012
Series ISSN: 2191-8112
Series E-ISSN: 2191-8120
Edition Number: 1
Number of Pages: IX, 100
Number of Illustrations: 58 b/w illustrations
Topics: Computational Biology/Bioinformatics, Bioinformatics, Algorithm Analysis and Problem Complexity