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.
Introduction. Knowledge Management. Current Status and Future Perspectives of Mass Spectrometry. Graph Theory Analysis of Protein--Protein Interactions. HTP Protein Crystallization Approaches. Integration of Diverse Data, Algorithms, and Domains. From High-Throughput to Systems Biology. References.