Cancer Informatics in Post-Genomic Era provides both the necessary methodology and practical information tools for analyzing data in the field of medical information science. This, of course, requires analytic tools. Those tools are garnered by developing and assessing methods and systems for the acquisition, processing, and interpretation of patient data, aided by scientific discovery. Key challenges in this field include integrating research and clinical care, sharing data, and establishing partnerships within and across sectors of patient diagnosis and treatment.

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...

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...