PostGenome Bio Data Management: Modeling and Applications

PostGenome Bio Data Management: Modeling and Applications

ISBN-10:
1596932589
ISBN-13:
9781596932586
Pub. Date:
10/31/2007
Publisher:
Artech House, Incorporated
ISBN-10:
1596932589
ISBN-13:
9781596932586
Pub. Date:
10/31/2007
Publisher:
Artech House, Incorporated
PostGenome Bio Data Management: Modeling and Applications

PostGenome Bio Data Management: Modeling and Applications

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Overview

Morden biological resereach 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 collectionof next-generation biodata modeling techiques combines innovative concepts, methods, and applications with case studies in genome, microarry, proteomics, adn drug discovery projects to help bioinformatics professionals develop ever-more powerful data management systems in any domain. Breaking new ground at the intersection of life sciences and data management, the book introduces practitioners to core biodata modeling techniques, biological database resources, adn ontology concepts. It explains the latest envelope-pushing methods and software applications for processing, integrating, and managing biodata.


Product Details

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

About 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.

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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