Description
Knowledge Discovery in Proteomics
Authors: Jurisica Igor, Wigle Dennis
Language: EnglishSubjects for Knowledge Discovery in Proteomics:
Keywords
PPI Network; PPI Data; SH3 Domain; Scale Free Network; LC MS Experiment; Random Graph; MALDI ToF MS; Protein Crystallization; Interacting Protein Pairs; Degree Distribution; Peptidyl Prolyl Cistrans Isomerase; HTP Dataset; Power Law Degree Distribution; Tandem Mass Spectra; Genbank Id; Ras Guanine Nucleotide Exchange Factor; Protein Pairs; Crystallization Experiments; Real World Networks; DNA Damage Response Pathway; Protein Protein Interaction Network Analysis; Support Vector Machines; Radon Transform; Metabolic Networks; PIs
160.25 €
Subject to availability at the publisher.
Add to cart the book of Jurisica Igor, Wigle DennisPublication date: 09-2005
· 15.6x23.4 cm · Hardback
Publication date: 10-2019
· 15.6x23.4 cm · Paperback
Description
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Multi-modal representations, the lack of complete and consistent domain theories, rapid evolution of domain knowledge, high dimensionality, and large amounts of missing information - these are challenges inherent in modern proteomics. As our understanding of protein structure and function becomes ever more complicated, we have reached a point where the actual management of data is a major stumbling block to the interpretation of results from proteomic platforms, to knowledge discovery.
Knowledge Discovery in Proteomics presents timely, authoritative discussions on some of the key issues in high-throughput proteomics, exploring examples that represent some of the major challenges of knowledge discovery in the field. The authors focus on five specific domains:
In each area, the authors describe the challenges created by the type of data produced and present potential solutions to the problem of data mining within the domain. They take a systems approach, covering individual data and integrating its computational aspects, from data preprocessing, storage, and access to analysis, visualization, and interpretation.
With clear exposition, practical examples, and rich illustrations, this book presents an outstanding overview of this emerging field, and builds the background needed for the fruitful exchange of ideas between computational and biological scientists.