PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Structured Output Prediction of Anti-cancer Drug Activity
Hongyu Su, Markus Heinonen and Juho Rousu
In: 5th International Conference on Pattern Recognition in Bioinformatics, 22-24 Sep 2010, Nijmegen, The Netherlands.

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.

EPrint Type:Conference or Workshop Item (Paper)
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Learning/Statistics & Optimisation
Theory & Algorithms
ID Code:8121
Deposited By:Juho Rousu
Deposited On:22 April 2011