Bioinformatics Methods in Clinical Research

Covering the latest developments in clinical omics, this volume details the algorithms currently used in publicly available software tools. It looks at statistics, algorithms, automated data retrieval, and experimental consideration in the various omics areas.

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.

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 a comprehensive coverage of biomedical text mining Includes supplementary material: sn.pub/extras

Inhalt
to Omics.- Machine Learning: An Indispensable Tool in Bioinformatics.- SNP-PHAGE: High-Throughput SNP Discovery Pipeline.- R Classes and Methods for SNP Array Data.- Overview on Techniques in Cluster Analysis.- Nonalcoholic Steatohepatitis, Animal Models, and Biomarkers: What Is New?.- Biomarkers in Breast Cancer.- Genome-Wide Proximal Promoter Analysis and Interpretation.- Proteomics Facing the Combinatorial Problem.- Methods and Algorithms for Relative Quantitative Proteomics by Mass Spectrometry.- Feature Selection and Machine Learning with Mass Spectrometry Data.- Computational Methods for Analysis of Two-Dimensional Gels.- Mass Spectrometry in Epigenetic Research.- Computational Approaches to Metabolomics.- Algorithms and Methods for Correlating Experimental Results with Annotation Databases.- Analysis of Biological Processes and Diseases Using Text Mining Approaches.