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Graph boosting for molecular QSAR analysis




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.

Author(s): Saigo, H. and Kadowaki, T. and Kudo, T. and Tsuda, K.
Year: 2006
Month: December
Day: 0

Department(s): Empirical Inference
Bibtex Type: Talk (talk)

Digital: 0
Event Name: NIPS 2006 Workshop on New Problems and Methods in Computational Biology
Event Place: Vancouver, BC, Canada
Language: en
Organization: Max-Planck-Gesellschaft
School: Biologische Kybernetik

Links: Web


  title = {Graph boosting for molecular QSAR analysis},
  author = {Saigo, H. and Kadowaki, T. and Kudo, T. and Tsuda, K.},
  organization = {Max-Planck-Gesellschaft},
  school = {Biologische Kybernetik},
  month = dec,
  year = {2006},
  month_numeric = {12}