Includes bibliographical references and index.

Introduction to omics / Ewa Gubb and Rune Matthiesen -- Machine learning : an indispensable tool in bioinformatics / Iñaki Inza [and others] -- SNP-PHAGE : high-throughput SNP discovery pipeline / Ana M. Aransay, Rune Matthiesen, and Manuela M. Regueiro -- R classes and methods for SNP array data / Robert B. Scharpf and Ingo Ruczinski -- Overview on techniques in cluster analysis / Itziar Frades and Rune Matthiesen -- Nonalcoholic steatohepatitis, animal models, and biomarkers : what is new? : / Usue Ariz [and others] -- Biomarkers in breast cancer / María dM. Vivanco -- Genome-wide proximal promoter analysis and interpretation / Elizabeth Guruceaga [and others] -- Proteomics facing the combinatorial problem / Rune Matthiesen and António Amorim -- Methods and algorithms for relative quantitative proteomics by mass spectrometry / Rune Matthiesen and Ana Sofia Carvalho -- Feature selection and machine learning with mass spectrometry data / Susmita Datta and Vasyl Pihur -- Computational methods for analysis of two-dimensional gels / Gorka Lasso and Rune Matthiesen -- Mass spectrometry in epigenetic research / Hans Christian Beck -- Computational approaches to metabolomics / David S. Wishart -- Algorithms and methods for correlating experimental results with annotation databases / Michael Hackenberg and Rune Matthiesen -- Analysis of biological processes and diseases using text mining approaches / Martin Krallinger, Florian Leitner, and Alfonso Valencia.

Print version record.

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.

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