Klaus Jung – författare

This valuable collection aims to provide a collection of frequently used statistical methods in the field of proteomics. Although there is a large overlap between statistical methods for the different ‘omics’ fields, methods for analyzing data from proteomics experiments need their own specific adaptations. To satisfy that need, Statistical Analysis in Proteomics focuses on the planning of proteomics experiments, the preprocessing and analysis of the data, the integration of proteomics data with other high-throughput data, as well as some special topics. Written for the highly successful Methods in Molecular Biology series, the chapters contain the kind of detail and expert implementation advice that makes for a smooth transition to the laboratory.Practical and authoritative, Statistical Analysis in Proteomics serves as an ideal reference for statisticians involved in the planning and analysis of proteomics experiments, beginners as well as advanced researchers, and also for biologists, biochemists, and medical researchers who want to learn more about the statistical opportunities in the analysis of proteomics data.

This valuable collection aims to provide a collection of frequently used statistical methods in the field of proteomics. Although there is a large overlap between statistical methods for the different ‘omics’ fields, methods for analyzing data from proteomics experiments need their own specific adaptations. To satisfy that need, Statistical Analysis in Proteomics focuses on the planning of proteomics experiments, the preprocessing and analysis of the data, the integration of proteomics data with other high-throughput data, as well as some special topics. Written for the highly successful Methods in Molecular Biology series, the chapters contain the kind of detail and expert implementation advice that makes for a smooth transition to the laboratory.Practical and authoritative, Statistical Analysis in Proteomics serves as an ideal reference for statisticians involved in the planning and analysis of proteomics experiments, beginners as well as advanced researchers, and also for biologists, biochemists, and medical researchers who want to learn more about the statistical opportunities in the analysis of proteomics data.

Interest and research in urinary enzymology were incited about three decades ago by reports that urinary enzymes are elevated in diseases of the kidney and urinary tract. Of the more than 40 hydrolases, oxidoreductases, transferases, and lyases identified in human and animal urine, only ten or so are being used as diagnostic indicators. Recognition of the quantitative distribution of enzymes in the various anatomical and functional parts of the nephron and advances in our understanding of the handling of proteins by the kidney have made it possible to associate urinary enzyme activity patterns with physiological and pathophysio­ logical functions of the nephron. Confidence in the diagnostic value of urinary enzymes is not unanimous among clinicians and among scientists. The main reason for the difference in opinion may well be that the variability in data exceeds the variability one is accustomed to in the diagnostic enzymology of blood plasma enzymes. In contrast to plasma enzymes, which are protected by an "enzyme friendly" milieu, enzymes released into the urine encounter an "enzyme hostile" environ­ ment: no or little protective protein, variable pH, variable volume, variable metabolite and salt concentrations, variable concentrations of enzyme in­ hibitors. Through advances in methodology some of these factors can now be controlled; standardization of urine collection periods and preanalytical treat­ ment are as important as optimization of assay methods.