Abstract
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.
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Keywords
- Support Vector Machine
- Inductive Logic Programming
- Support Vector Machine Method
- Markov Network
- Graph Kernel
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Su, H., Heinonen, M., Rousu, J. (2010). Structured Output Prediction of Anti-cancer Drug Activity. In: Dijkstra, T.M.H., Tsivtsivadze, E., Marchiori, E., Heskes, T. (eds) Pattern Recognition in Bioinformatics. PRIB 2010. Lecture Notes in Computer Science(), vol 6282. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-16001-1_4
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DOI: https://doi.org/10.1007/978-3-642-16001-1_4
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