Graph boosting for molecular QSAR analysis

Saigo, H; Kadowaki , T; Kudo, T; Tsuda, K

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

Genre: Talk

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http://raetschlab.org:10080/nipscompbio/previous/MLCB2006/schedule06 (Table of contents) Open Access status unknown

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Creators:
Saigo, H^{1, 2}, Author
Kadowaki , T, Author
Kudo, T, Author
Tsuda, K^{1, 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: Date issued: 2006-12

Publication Status: Issued

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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

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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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