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  • Open Access

Data integration and knowledge transfer: application to the tissue: air partition coefficients

  • 1, 2,
  • 1,
  • 2,
  • 3,
  • 4 and
  • 1
Chemistry Central Journal20093 (Suppl 1) :P30

https://doi.org/10.1186/1752-153X-3-S1-P30

  • Published:

Keywords

  • Organic Compound
  • Partition Coefficient
  • Human Blood
  • Knowledge Transfer
  • Related Property

Conventional QSAR/QSPR models are built only for one target property without exploiting any a priori knowledge stored in datasets of related properties. Here, individual models are not viewed as separate entities but as nodes in the network of interrelated models. Such interrelated models can be built in parallel by means of multitask learning (MTL), or sequentially using feature nets (FN). MTL and FN are kinds of data integration, as opposed to traditional single-task learning (STL), in which all models are built separately. We apply this strategy to model Human blood:air, human and rat tissue:air partition coefficients of organic compounds using diverse and relatively small datasets.

Authors’ Affiliations

(1)
Laboratoire d'Infochimie (UMR 7177 CNRS), Strasbourg, France
(2)
Technologie Servier, Orléans, France
(3)
GSF-Institute for Bioinformatics, Neuherberg, Germany
(4)
Department of Chemistry, Moscow State University, Moscow, Russia

Copyright

© Gaudin et al; licensee BioMed Central Ltd. 2009

This article is published under license to BioMed Central Ltd.

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