Bioinformatics Methods in Clinical Research By Matthiesen, Rune (Ed.)

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Bioinformatics Methods in Clinical Research

Series: Methods in Molecular Biology, Vol. 593

Matthiesen, Rune (Ed.)

2010

 

  • Fully updated overview on machine learning techniques applied to biological problems
  • Includes detailed information about current standards for a number of clinical diseases
  • Presents details on data analysis strategies in genomics, transcriptomics, proteomics and metabolomics
  • Summarizes statistical methods and tools for enrichment/depletion analysis
  • Provides comprehensive coverage of biomedical text mining

Integrated bioinformatics solutions have become increasingly valuable in past years, as technological advances have allowed researchers to consider the potential of omics for clinical diagnosis, prognosis, and therapeutic purposes, and as the costs of such techniques have begun to lessen.  In Bioinformatics Methods in Clinical Research, experts examine the latest developments impacting clinical omics, and describe in great detail the algorithms that are currently used in publicly available software tools. Chapters discuss statistics, algorithms, automated methods of data retrieval, and experimental consideration in genomics, transcriptomics, proteomics, and metabolomics. Composed in the highly successful Methods in Molecular Biology™ series format, each chapter contains a brief introduction, provides practical examples illustrating methods, results, and conclusions from data mining strategies wherever possible, and includes a Notes section which shares tips on troubleshooting and avoiding known pitfalls.

Informative and ground-breaking, Bioinformatics Methods in Clinical Research establishes a much-needed bridge between theory and practice, making it an indispensable resource for bioinformatics researchers.

Content Level » Research

Keywords » Annotation - Anästhesie-Informations-Management-System - Automated data retrieval - Bioinformatics algorithms - Mascot - Metabolomics - Proteomics - System biology - Transcriptomics - algorithms - bioinformatics - classification - cluster analysis - data mining - databases

 

Contents

Preface . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . v
Contributors . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ix

1. Introduction to Omics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1
Ewa Gubb and Rune Matthiesen

2. Machine Learning: An Indispensable Tool in Bioinformatics . . . . . . . . . . . 25
I˜naki Inza, Borja Calvo, Rub´en Arma˜nanzas, Endika Bengoetxea,
Pedro Larra˜naga, and Jos´e A. Lozano

3. SNP-PHAGE: High-Throughput SNP Discovery Pipeline . . . . . . . . . . . . 49
Ana M. Aransay, Rune Matthiesen, and Manuela M. Regueiro

4. R Classes and Methods for SNP Array Data . . . . . . . . . . . . . . . . . . . . 67
Robert B. Scharpf and Ingo Ruczinski

5. Overview on Techniques in Cluster Analysis . . . . . . . . . . . . . . . . . . . 81
Itziar Frades and Rune Matthiesen

6. Nonalcoholic Steatohepatitis, Animal Models, and Biomarkers: What Is New? . . 109
Usue Ariz, Jose Maria Mato, Shelly C. Lu,
and Maria L. Mart´ınez Chantar

7. Biomarkers in Breast Cancer . . . . . . . . . . . . . . . . . . . . . . . . . . . 137
Mar´ıa dM. Vivanco

8. Genome-Wide Proximal Promoter Analysis and Interpretation . . . . . . . . . . 157
Elizabeth Guruceaga, Victor Segura, Fernando J. Corrales,
and Angel Rubio

9. Proteomics Facing the Combinatorial Problem . . . . . . . . . . . . . . . . . . 175
Rune Matthiesen and Ant´onio Amorim

10. Methods and Algorithms for Relative Quantitative Proteomics by Mass
Spectrometry . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 187
Rune Matthiesen and Ana Sofia Carvalho

11. Feature Selection and Machine Learning with Mass Spectrometry Data . . . . . 205
Susmita Datta and Vasyl Pihur

12. Computational Methods for Analysis of Two-Dimensional Gels . . . . . . . . . 231
Gorka Lasso and Rune Matthiesen

13. Mass Spectrometry in Epigenetic Research . . . . . . . . . . . . . . . . . . . . 263
Hans Christian Beck

14. Computational Approaches to Metabolomics . . . . . . . . . . . . . . . . . . . 283
David S. Wishart

15. Algorithms and Methods for Correlating Experimental Results
with Annotation Databases . . . . . . . . . . . . . . . . . . . . . . . . . . . . 315
Michael Hackenberg and Rune Matthiesen

16. Analysis of Biological Processes and Diseases Using Text Mining Approaches . . 341
Martin Krallinger, Florian Leitner, and Alfonso Valencia
Subject Index . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 383

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