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  Graph boosting for molecular QSAR analysis

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 (MLCB 2006). Vancouver, BC, Canada. 2006-12-08.

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Item Permalink: http://hdl.handle.net/11858/00-001M-0000-0013-CF5B-3 Version Permalink: http://hdl.handle.net/21.11116/0000-0004-C933-0
Genre: Talk

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 Creators:
Saigo, H1, 2, Author              
Kadowaki , T, Author
Kudo, T, Author
Tsuda, K1, 2, Author              
Affiliations:
1Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society, ou_1497795              
2Max Planck Institute for Biological Cybernetics, Max Planck Society, Spemannstrasse 38, 72076 Tübingen, DE, ou_1497794              

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 Abstract: 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.

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 Dates: 2006-12
 Publication Status: Published in print
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 Identifiers: BibTex Citekey: 5011
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Title: NIPS 2006 Workshop on New Problems and Methods in Computational Biology (MLCB 2006)
Place of Event: Vancouver, BC, Canada
Start-/End Date: 2006-12-08
Invited: Yes

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Title: NIPS 2006 Workshop on New Problems and Methods in Computational Biology (MLCB 2006)
Source Genre: Proceedings
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