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 biology 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.

One of the grand challenges in our digital world are the large, complex and often weakly structured data sets, and massive amounts of unstructured information. This "big data" challenge is most evident in biomedical informatics: the trend towards precision medicine has resulted in an explosion in the amount of generated biomedical data sets. Despite the fact that human experts are very good at pattern recognition in dimensions of <= 3; most of the data is high-dimensional, which makes manual analysis often impossible and neither the medical doctor nor the biomedical researcher can memorize all these facts. A synergistic combination of methodologies and approaches of two fields offer ideal conditions towards unraveling these problems: Human-Computer Interaction (HCI) and Knowledge Discovery/Data Mining (KDD), with the goal of supporting human capabilities with machine learning. This state-of-the-art survey is an output of the HCI-KDD expert network and features 19 carefully selected and reviewed papers related to seven hot and promising research areas: Area 1: Data Integration, Data Pre-processing and Data Mapping; Area 2: Data Mining Algorithms; Area 3: Graph-based Data Mining; Area 4: Entropy-Based Data Mining; Area 5: Topological Data Mining; Area 6 Data Visualization and Area 7: Privacy, Data Protection, Safety and Security.

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. The 10 revised papers included in this volume were carefully reviewed and selected from 23 submissions. The papers cover a range of important topics such as algorithms for ontology matching, interoperable frameworks for text mining using semantic web services, pipelines for genome-wide functional annotation, automation of pipelines providing data discovery and access to distributed resources, knowledge-driven querying-answer systems, prizms, nanopublications, electronic health records and linked data.

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.

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 biology 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.