Mathematica for Bioinformatics : A Wolfram Language Approach to Omics / by George Mias.
Mias, George. author.
2018
QH324.2-324.25
Available Online

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Title Mathematica for Bioinformatics : A Wolfram Language Approach to Omics / by George Mias.
Author Mias, George. author.
ISBN 9783319723778
3319723774
9783319723761
3319723766
DOI https://doi.org/10.1007/978-3-319-72377-8
Published Cham : Springer International Publishing : Imprint: Springer, 2018.
Language English
Description 1 online resource (xvi, 384 pages) : illustrations.
Other Standard Identifiers 10.1007/978-3-319-72377-8 doi
Call Number QH324.2-324.25
Dewey Decimal Classification 570.285
Summary This book offers a comprehensive introduction to using Mathematica and the Wolfram Language for Bioinformatics. The chapters build gradually from basic concepts and the introduction of the Wolfram Language and coding paradigms in Mathematica, to detailed worked examples derived from typical research applications using Wolfram Language code. The coding examples range from basic sequence analysis, accessing genomic databases, and differential gene expression, to time series analysis of longitudinal omics experiments, multi-omics integration and building dynamic interactive bioinformatics tools using the Wolfram Language. The topics address the daily bioinformatics needs of a broad audience: experimental users looking to understand and visualize their data, beginner bioinformaticians acquiring coding expertise in providing biological research solutions, and practicing expert bioinformaticians working on omics who wish to expand their toolset to include the Wolfram Language.
Bibliography, etc. Note Includes bibliographical references and index.
Access Note Access limited to authorized users.
Digital File Characteristics text file PDF
Available in Other Form Print version: 9783319723761
Linked Resources Online Access
Record Appears in Online Resources > Ebooks
All Resources
1 Introduction to Bioinformatics
2. A Mathematica Primer for Bioinformaticians
3. Statistics for Genomic Analysis
4. Genomic Sequences
5. Databases
6. Transcriptomics
7. Proteomics
8. Metabolomics
9. Systems Biology
10. Networks
11. Time Series Analysis
12. Omics Integration and Systems Medicine
13. Bioinformatics Development with Mathematica.

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