Machine Learning for Biomedical Knowledge Discovery

Description

This workshop will try to borrow and adapt diverse theoretical innovations on probabilistic models and related machine learning methods from other areas and will focus on probabilistic-based data mining methods, including graph-based data mining, topological data mining and other information-theoretic-based approaches (e.g., entropy-based data mining), as well as on the “human-in-the-loop” concept, supported by an interactive learning and optimization component and in visual analysis of heterogeneous and dynamic data sets. For example in network-based approaches, statistical extensions of graph theoretical approaches, visualizing networks, epistemological meaning of inferred networks, structural analysis of networks, comparative analysis of networks and network-based biomarkers are challenges, to mention only a few. Classical mathematical techniques do often not fit well the task of analyzing, comparing, classifying, retrieving complex data sets. Topology (and in particular algebraic topology) is, by its very nature, the part of mathematics which formalizes qualitative aspects of objects; therefore topological data processing and topological data mining well integrates with more classical mathematical tools. For example, persistent homology combines geometry and algebraic topology in the study of pairs (X,f) where X is an object (topological space) and f is a continuous function defined on X (typically with real values). One application is the extraction of topological features of an object out of a cloud of sample points. Features are key to learning and understanding. Another class of applications uses f as a formalization of a classification criterion; in this case various functions can give different criteria, cooperating in a complex classifier. Several problems arise from such settings: One, in the application context, is the choice of suitable functions f. This is generally done heuristically, but it would be necessary to have parametrized spaces of such functions and eventually a self-driving, optimized choice of f for statistical learning. Another challenge is the construction of good distances. The ones presently available need exponential computation. A third problem concerns functions with multidimensional range: functions from X to R give rise to diagrams whose information is condensed in a discrete (mostly finite) set of points in the plane; but if the range is R^k, the same information is carried by (2k-2) dimensional patches in R^2k. A one-dimensional reduction is available, but it raises computational problems in applications.

Period	24 Jul 2015 - 26 Jul 2015
Event type	Conference
Conference number	BIRS-15w2181
Location	Banff, Canada