Ebook: Network Inference in Molecular Biology: A Hands-on Framework

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.

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Content:
Front Matter....Pages i-ix
Overview of Network Inference....Pages 1-9
Clustering Data....Pages 11-22
Step 2: Use Steady State Data for Network Inference....Pages 23-50
Step 3: Using Time-Series Data....Pages 51-76
Pipelines....Pages 77-95
Back Matter....Pages 97-100

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Content:
Front Matter....Pages i-ix
Overview of Network Inference....Pages 1-9
Clustering Data....Pages 11-22
Step 2: Use Steady State Data for Network Inference....Pages 23-50
Step 3: Using Time-Series Data....Pages 51-76
Pipelines....Pages 77-95
Back Matter....Pages 97-100
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