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PRIB
2010
Springer

Structured Output Prediction of Anti-cancer Drug Activity

11 years 3 months ago
Structured Output Prediction of Anti-cancer Drug Activity
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.
Hongyu Su, Markus Heinonen, Juho Rousu
Added 29 Jan 2011
Updated 29 Jan 2011
Type Journal
Year 2010
Where PRIB
Authors Hongyu Su, Markus Heinonen, Juho Rousu
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