9783662659014-3662659018-Handbook of Statistical Bioinformatics (Springer Handbooks of Computational Statistics)

Handbook of Statistical Bioinformatics (Springer Handbooks of Computational Statistics)

ISBN-13: 9783662659014
ISBN-10: 3662659018
Edition: 2nd ed. 2022
Author: Henry Horng-Shing Lu, Bernhard Schölkopf, Martin T. Wells, Hongyu Zhao
Publication date: 2022
Publisher: Springer
Format: Hardcover 418 pages
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ISBN-13: 9783662659014
ISBN-10: 3662659018
Edition: 2nd ed. 2022
Author: Henry Horng-Shing Lu, Bernhard Schölkopf, Martin T. Wells, Hongyu Zhao
Publication date: 2022
Publisher: Springer
Format: Hardcover 418 pages

Summary

Handbook of Statistical Bioinformatics (Springer Handbooks of Computational Statistics) (ISBN-13: 9783662659014 and ISBN-10: 3662659018), written by authors Henry Horng-Shing Lu, Bernhard Schölkopf, Martin T. Wells, Hongyu Zhao, was published by Springer in 2022. With an overall rating of 4.1 stars, it's a notable title among other Databases & Big Data (Mathematical & Statistical, Software, Anatomy, Biological Sciences, Bioinformatics) books. You can easily purchase or rent Handbook of Statistical Bioinformatics (Springer Handbooks of Computational Statistics) (Hardcover) from BooksRun, along with many other new and used Databases & Big Data books and textbooks. And, if you're looking to sell your copy, our current buyback offer is $0.3.

Description

Now in its second edition, this handbook collects authoritative contributions on modern methods and tools in statistical bioinformatics with a focus on the interface between computational statistics and cutting-edge developments in computational biology. The three parts of the book cover statistical methods for single-cell analysis, network analysis, and systems biology, with contributions by leading experts addressing key topics in probabilistic and statistical modeling and the analysis of massive data sets generated by modern biotechnology. This handbook will serve as a useful reference source for students, researchers and practitioners in statistics, computer science and biological and biomedical research, who are interested in the latest developments in computational statistics as applied to computational biology.

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