A survey of kernels for structured data
ACM SIGKDD Explorations Newsletter
Regularized multi--task learning
Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
Support vector machine learning for interdependent and structured output spaces
ICML '04 Proceedings of the twenty-first international conference on Machine learning
Weighted decomposition kernels
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
Kernel-Based Learning of Hierarchical Multilabel Classification Models
The Journal of Machine Learning Research
Multi-task drug bioactivity classification with graph labeling ensembles
PRIB'11 Proceedings of the 6th IAPR international conference on Pattern recognition in bioinformatics
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We present a structured output prediction approach for classifying potential anti-cancer drugs. Our QSAR model takes as input a description of a molecule and predicts the activity against a set of cancer cell lines in one shot. Statistical dependencies between the cell lines are encoded by a Markov network that has cell lines as nodes and edges represent similarity according to an auxiliary dataset. Molecules are represented via kernels based on molecular graphs. Margin-based learning is applied to separate correct multilabels from incorrect ones. The performance of the multilabel classification method is shown in our experiments with NCI-Cancer data containing the cancer inhibition potential of drug-like molecules against 59 cancer cell lines. In the experiments, our method outperforms the state-of-the-art SVM method.