From the reviews: "This volume of Methods in Molecular Biology provides an overview of the recent improvements in the use of bioinformatics in clinical research. ... All chapters provide pertinent examples. ... The book is intended primarily for scientists involved in biomedical informatics (computational biologists, biostatisticians, computer scientists) who are active in the field of clinical research. This includes both first-time and seasoned students and biomedical researchers who are interested in using bioinformatics. ... It also presents a number of valuable approaches currently in use." (Ruben Mestril, Doody's Review Service, April, 2010) "This book 'Bioinformatics Methods in Clinical Research edited by Rune Matthiesen' contains sixteen chapters, and results from scientific cooperation of academics, specialists in the main areas of investigation on bioinformatics methods, and applications in clinical research from centers and universities of Europe such as Denmark, Spain, Portugal as well as Canada and the United States of North America. ... several specific fields are discussed including biomarkers in breast cancer and computational methods for two-dimensional analyses in gels." (Silvia Regina Cavani Jorge Santos, Brazilian Journal of Pharmaceutical Sciences, Spring, 2011)

• 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.

From the reviews: "This volume of Methods in Molecular Biology provides an overview of the recent improvements in the use of bioinformatics in clinical research. ... All chapters provide pertinent examples. ... The book is intended primarily for scientists involved in biomedical informatics (computational biologists, biostatisticians, computer scientists) who are active in the field of clinical research. This includes both first-time and seasoned students and biomedical researchers who are interested in using bioinformatics. ... It also presents a number of valuable approaches currently in use." (Ruben Mestril, Doody's Review Service, April, 2010) "This book 'Bioinformatics Methods in Clinical Research edited by Rune Matthiesen' contains sixteen chapters, and results from scientific cooperation of academics, specialists in the main areas of investigation on bioinformatics methods, and applications in clinical research from centers and universities of Europe such as Denmark, Spain, Portugal as well as Canada and the United States of North America. ... several specific fields are discussed including biomarkers in breast cancer and computational methods for two-dimensional analyses in gels." (Silvia Regina Cavani Jorge Santos, Brazilian Journal of Pharmaceutical Sciences, Spring, 2011)

• 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.

From the reviews: "This volume of Methods in Molecular Biology provides an overview of the recent improvements in the use of bioinformatics in clinical research. ... All chapters provide pertinent examples. ... The book is intended primarily for scientists involved in biomedical informatics (computational biologists, biostatisticians, computer scientists) who are active in the field of clinical research. This includes both first-time and seasoned students and biomedical researchers who are interested in using bioinformatics. ... It also presents a number of valuable approaches currently in use." (Ruben Mestril, Doody's Review Service, April, 2010) "This book 'Bioinformatics Methods in Clinical Research edited by Rune Matthiesen' contains sixteen chapters, and results from scientific cooperation of academics, specialists in the main areas of investigation on bioinformatics methods, and applications in clinical research from centers and universities of Europe such as Denmark, Spain, Portugal as well as Canada and the United States of North America. ... several specific fields are discussed including biomarkers in breast cancer and computational methods for two-dimensional analyses in gels." (Silvia Regina Cavani Jorge Santos, Brazilian Journal of Pharmaceutical Sciences, Spring, 2011)

• 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.