Master the next-generation of database modeling techniques with this cutting-edge resource and take your biodata management skills to the next level. The book shows you how to take a global approach to representing complex biological concepts and their evolving data structures. This highly accessible volume presents innovative biological database modeling concepts, methods, and software tools that are integrated with case studies in genomics, functional genomics, proteomics, and drug discovery projects. Breaking new ground at the intersection of high-throughput biology, bioinformatics, and data management, this essential resource offers you a comprehensive introduction to biological data modeling techniques, biological database resources, and ontology concepts. You are introduced to the latest computational methods and software applications for processing, integrating, and managing biological data from a variety of sources, including public literature, high-throughput sequencing, gene expression profiling, proteomics profiling, and chemical compound screenings. Throughout the book, novel perspectives are presented to deal with challenges inherent in biological data, including Omicsù data-high volume, high noise, inconsistent format, incompleteness, and semantic incompatibility. Supported with more than 50 illustrations, this unique reference serves as a practical guide to your current and future bioinformatics data management projects, enabling you to design and implement effective biological data management and workflow systems.
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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Jake Chen
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.) and bioinformatics computer scientist at Affymetrix, Inc. Dr. Chen has published many research papers in bioinformatics and presented talks on biological data management and systems biology at leading research institutes, universities, and conferences worldwide. He received his Ph.D. in computer science from the University of Minnesota and B.S. in biochemistry and Molecular Biology from Peking University, China.
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Amandeep S. Sidhu
Amandeep S. Sidhu is a researcher at Digital Ecosystems and the Business Intelligence Institute, Curtain University of Technology. He is also a Ph.D. candidate at the University of Technology, Sydney, where he is studying in the area of biomedical onthologies. He holds an M.Sc. in computer science from La Trobe University.