Igor Jurisica – författare

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:Mass spectrometry-based protein analysis Protein-protein interaction network analysis Systematic high-throughput protein crystallization Systematic, integrated analysis of multiple data repositories Systems biologyIn 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.

Provides both the necessary methodology and practical information tools for analyzing data in the field of medical information science.

Medical information science requires analytic tools. This is achieved by developing and assessing methods and systems for the acquisition, processing, and interpretation of patient data, aided by scientific discovery. Cancer Informatics in Post-Genomic Era provides both the necessary methodology and practical information tools.Key challenges include integrating research and clinical care, sharing data, and establishing partnerships within and across sectors of patient diagnosis and treatment.Addressing important clinical questions in cancer research will benefit from expanding computational biology.The advent of genomic and proteomic technologies has ushered forth the era of genuine medicine. The promise of these advances is true "personalized medicine" where treatment strategies can be individually tailored and advance to initiating intervention before visible symptoms appear.

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:Mass spectrometry-based protein analysis Protein-protein interaction network analysis Systematic high-throughput protein crystallization Systematic, integrated analysis of multiple data repositories Systems biologyIn 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.

This book constitutes the refereed proceedings of the 9th International Conference on Data Integration in the Life Sciences, DILS 2013, held in Montreal, QC, Canada, in July 2013.