PostGenome Bio Data Management: Modeling and Applications

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Overview

Modern biological research in areas like drug discovery produces a staggering volume of data, and the right modeling tools can help scientists apply it in ways never before imaginable. This collection of next-generation biodata modeling techniques combines innovative concepts, methods, and applications with case studies in genome, microarray, proteomics, and drug discovery projects that helps bioinformatics professionals develop ever-more powerful data management systems in any domain.

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Product Details

  • ISBN-13: 9781596932586
  • Publisher: Artech House, Incorporated
  • Publication date: 10/31/2007
  • Edition description: New Edition
  • Pages: 224
  • Product dimensions: 7.10 (w) x 10.20 (h) x 0.70 (d)

Meet the Author

Jake Chen is an assistant professor in the School of Informatics at Indiana University and an assistant professor of computer science at Purdue University. Previously he was the head of computational proteomics at Myriad Proteomics, Inc. (now Prolexys Pharmaceuticals, Inc.) Dr. Chen has presented papers at leading biotech firms, research institutes, universities, ACM/IEEE meetings, and national and international conferences. He received his Ph.D. in computer science from the University of Minnesota.

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Table of Contents

Introduction. Public Biological Databases For Omics Studies in Medicine. Fundamentals of Gene Ontology. Protein Ontology. Information Quality Management Challenges for High-Throughput Data. Data Management for Fungal Genomics: An Experience Report. Microarray Data Management. Data Management in Expression-Based Proteomics. Model Driven Drug Discovery: Principles and Practices. Information Management and Interaction in High-Throughput Screening for Drug Discovery. Modeling Concepts and Database Implementation Techniques for Complex Biological Data.

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