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Informationen zum Autor Sudhir Srivastava Klappentext A well-rounded resource for those involved with informatics tools useful in disease detection! diagnosis! and treatment! Informatics in Proteomics examines the application of statistical and bioinformatic tools in data analysis! presentation! and mining. It discusses the implementation of algorithms! statistical methods! and computer applications that facilitate pattern recognition and biomarker discovery by integrating data from multiple sources. The book also addresses the infrastructure needed for public protein databases! information management systems and user interfaces! as well as issues surrounding data standardization and integration of protein sequences recorded in the last two decades. Zusammenfassung Examines the advances in the application of bioinformatics to proteomics research and analysis. This book addresses the infrastructure needed for public protein databases. It discusses information management systems and user interfaces for storage, retrieval, and visualization of the data. Inhaltsverzeichnis The Promise of Proteomics: Biology! Applications! and Challenges; Proteomics Technologies; Creating a National Virtual Knowledge Environment for Proteomics and Information Management; Public Protein Databases and Interfaces; Proteomics Knowledge Databases: Facilitating Collaboration and Interaction between Academia! Industry! and Federal Agencies; Proteome Knowledge Bases in the Context of Cancer; Data Standards in Proteomics: Promises and Challenges; Data Standardization and Integration in Collaborative Proteomics Studies; Informatics Tools for Functional Pathway Analysis Using Genomics and Proteomics; Data Mining in Proteomics; Protein Expression Analysis; Nonparametric! Distance-Based! Supervised Protein Array Analysis; Protein Identification by Searching Collection of Sequences with Mass Spectrometric Data; Bioinformatics Tools for Differential Analysis of Proteomic Expression Profiling Data from Clinical Samples; Sample Characterization Using Large Data Sets; Computational Tools for Tandem Mass Spectrometry-Based High-Throughput Quantitative Proteomics; Pattern Recognition Algorithms and Disease Biomarkers; Statistical Design and Analytical Strategies for Discovery of Disease-Specific Protein Patterns; Image Analysis in Proteomics; Index ...
