Interactive Knowledge Discovery and Data Mining in Biomedical Informatics [electronic resource] : State-of-the-Art and Future Challenges /

Knowledge Discovery and Data Mining in Biomedical Informatics: The Future Is in Integrative, Interactive Machine Learning Solutions -- Visual Data Mining: Effective Exploration of the Biological Universe -- Darwin or Lamarck? Future Challenges in Evolutionary Algorithms for Knowledge Discovery and Data Mining -- On the Generation of Point Cloud Data Sets: Step One in the Knowledge Discovery Process -- Adapted Features and Instance Selection for Improving Co-training -- Knowledge Discovery and Visualization of Clusters for Erythromycin Related Adverse Events in the FDA Drug Adverse Event Reporting System -- On Computationally-Enhanced Visual Analysis of Heterogeneous Data and Its Application in Biomedical Informatics -- A Policy-Based Cleansing and Integration Framework for Labour and Healthcare Data -- Interactive Data Exploration Using Pattern Mining -- Resources for Studying Statistical Analysis of Biomedical Data and R -- A Kernel-Based Framework for Medical Big-Data Analytics -- On Entropy-Based Data Mining -- Sparse Inverse Covariance Estimation for Graph Representation of Feature Structure -- Multi-touch Graph-Based Interaction for Knowledge Discovery on Mobile Devices: State-of-the-Art and Future Challenges -- Intelligent Integrative Knowledge Bases: Bridging Genomics, Integrative Biology and Translational Medicine -- Biomedical Text Mining: State-of-the-Art, Open Problems and Future Challenges -- Protecting Anonymity in Data-Driven Biomedical Science -- Biobanks – A Source of Large Biological Data Sets: Open Problems and Future Challenges -- On Topological Data Mining.

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 toward precision medicine has resulted in an explosion in the amount of biomedical data sets generated. Despite the fact that human experts are very good at pattern recognition in three dimensions or less, most of the data are 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 the methodologies and approaches of two fields offer ideal conditions for 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: (1) data integration, data pre-processing, and data mapping; (2) data mining algorithms; (3) graph-based data mining; (4) entropy-based data mining; (5) topological data mining; (6)  visualization; (7) privacy, data protection, safety, and security.    .