Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond
Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond
Optimal assignment kernels for attributed molecular graphs
ICML '05 Proceedings of the 22nd international conference on Machine learning
2005 Speical Issue: Graph kernels for chemical informatics
Neural Networks - Special issue on neural networks and kernel methods for structured domains
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A recently published study showed the feasibility of chronic rat toxicity prediction, an important task to reduce the number of animal experiments using the knowledge of previous experiments. We benchmarked various kernel learning approaches for the prediction of chronic toxicity on a set of 565 chemical compounds, labeled with the Lowest Observed Adverse Effect Level, and achieved a prediction error close to the interlaboratory reproducibility. *** -Support Vector Regression was used in combination with numerical molecular descriptors and the Radial Basis Function Kernel, as well as with graph kernels for molecular graphs, to train the models. The results show that a kernel approach improves the Mean Squared Error and the Squared Correlation Coefficient using leave-one-out cross-validation and a seeded 10-fold-cross-validation averaged over 10 runs. Compared to the state-of-the-art, the Mean Squared Error was improved up to MSEloo of 0.45 and MSEcv of 0.46±0.09 which is close to the theoretical limit of the estimated interlaboratory reproducibility of 0.41. The Squared Empirical Correlation Coefficient was improved to $\text{Q}^2_{\text{loo}}$ of 0.58 and $\text{Q}^2_{\text{\text{cv}}}$ of 0.57±0.10. The results show that numerical kernels and graph kernels are both suited for predicting chronic rat toxicity for unlabeled compounds.