Interactive Knowledge Discovery and Data Mining in Biomedical Informatics

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="">

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.