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Vortrag

Graph boosting for molecular QSAR analysis

MPG-Autoren
http://pubman.mpdl.mpg.de/cone/persons/resource/persons84183

Saigo,  H
Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society;

http://pubman.mpdl.mpg.de/cone/persons/resource/persons84265

Tsuda,  K
Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society;

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Zitation

Saigo, H., Kadowaki T, Kudo, T., & Tsuda, K. (2006). Graph boosting for molecular QSAR analysis. Talk presented at NIPS 2006 Workshop on New Problems and Methods in Computational Biology. Vancouver, BC, Canada.


Zitierlink: http://hdl.handle.net/11858/00-001M-0000-0013-CF5B-3
Zusammenfassung
We propose a new boosting method that systematically combines graph mining and mathematical programming-based machine learning. Informative and interpretable subgraph features are greedily found by a series of graph mining calls. Due to our mathematical programming formulation, subgraph features and pre-calculated real-valued features are seemlessly integrated. We tested our algorithm on a quantitative structure-activity relationship (QSAR) problem, which is basically a regression problem when given a set of chemical compounds. In benchmark experiments, the prediction accuracy of our method favorably compared with the best results reported on each dataset.