Structured Output Prediction of Anti-Cancer Drug Activity
published: Oct. 14, 2010, recorded: September 2010, views: 2675
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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.
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