Extravaganza tutorial on hot ideas for interactive knowledge discovery and data mining in biomedical informatics

Abstract

Biomedical experts are confronted with "Big data", driven by the trend towards precision medicine. Despite the fact that humans are excellent at pattern recognition in dimensions of ≤ 3, most biomedical data is in dimensions much higher than 3, making manual analysis often impossible. Experts in daily routine are decreasingly capable of dealing with such data. Efficient, useable and useful computational methods, algorithms and tools to interactively gain insight into such data are a commandment of the time. A synergistic combination of methodologies of two areas may be of great help here: Human-Computer Interaction (HCI) and Knowledge Discovery/Data Mining (KDD), with the goal of supporting human intelligence with machine learning. Mapping higher dimensional data into lower dimensions is a major task in HCI, and a concerted effort including recent advances from graph-theory and algebraic topology may contribute to finding solutions. Moreover, much biomedical data is sparse, noisy and time-dependent, hence entropy is also amongst promising topics. This tutorial gives an overview of the HCI-KDD approach and focuses on 3 topics: graphs, topology and entropy. The goal of this intro tutorial is to motivate and stimulate further research. © 2014 Springer International Publishing.