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Open Access Green as soon as Postprint is submitted to ZB.
Inductive transfer of knowledge: Application of multi-task learning and feature net approaches to model tissue-air partition coefficients.
J. Chem. Inf. Model. 49, 133-144 (2009)
Two inductive knowledge transfer approaches - multitask learning (MTL) and Feature Net (FN) - have been used to build predictive neural networks (ASNN) and PLS models for I I types of tissue-air partition coefficients (TAPC). Unlike conventional single-task learning (STL) modeling focused only on a single target property without any relations to other properties, in the framework of inductive transfer approach, the individual models are viewed as nodes in the network of interrelated models built in parallel (MTL) or sequentially (FN). It has been demonstrated that MTL and FN techniques are extremely useful in structure-property modeling on small and structurally diverse data sets, when conventional STL modeling is unable to produce any predictive model. The predictive STL individual models were obtained for 4 out of I I TAPC, whereas application of inductive knowledge transfer techniques resulted in models for 9 TAPC. Differences in prediction performances of the models as a function of the machine-learning method, and of the number of properties simultaneously involved in the learning, has been discussed.
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Publication type
Article: Journal article
Document type
Scientific Article
Keywords
associative neural networks; structure-property; combinatorial library; cross-validation; data sets; prediction; database; bias; classification; lipophilicity
ISSN (print) / ISBN
0021-9576
e-ISSN
1520-5142
Quellenangaben
Volume: 49,
Issue: 1,
Pages: 133-144
Publisher
American Chemical Society (ACS)
Non-patent literature
Publications
Reviewing status
Peer reviewed