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Stefan Kramer, University Mainz, OH 14, E23

Event Date: June 6, 2013 16:15


New Algorithms for Graphs and Small Molecules:

Exploiting Local Structural Graph Neighborhoods and Target Label Dependencies

In the talk, I will present recently developed algorithms for predicting properties of graphs and small molecules: In the first part of the talk, I will present several methods exploiting local structural graph (similarity) neighborhoods: local models based on structural graph clusters, locally weighted learning, and the structural cluster kernel. In the second part, I will discuss methods that exploit label dependencies to improve the prediction of a large number of target labels, where the labels can be just binary (multi-label classification) or can again have a feature vector attached. The methods make use of Boolean matrix factorization and can be used to predict the effect of small molecules on biological systems.



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