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.
Original language | English |
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Title of host publication | Pattern Recognition in Bioinformatics |
Subtitle of host publication | 5th IAPR International Conference, PRIB 2010, Nijmegen, The Netherlands, September 22-24, 2010. Proceedings |
Editors | Tjeerd M. H. Dijkstra, Evgeni Tsivtsivadze, Elena Marchiori, Tom Heskes |
Publisher | Springer Verlag |
Pages | 38-49 |
ISBN (Electronic) | 978-3-642-16001-1 |
ISBN (Print) | 978-3-642-16000-4 |
DOIs | |
Publication status | Published - 2010 |
MoE publication type | A4 Article in a conference publication |
Event | IAPR International Conference on Pattern Recognition in Bioinformatics - Nijmegen, Netherlands Duration: 22 Sep 2010 → 24 Sep 2010 Conference number: 5 |
Publication series
Name | Lecture Notes in Computer Science |
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Publisher | Springer |
Volume | 6282 |
ISSN (Print) | 0302-9743 |
Conference
Conference | IAPR International Conference on Pattern Recognition in Bioinformatics |
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Abbreviated title | PRIB |
Country | Netherlands |
City | Nijmegen |
Period | 22/09/2010 → 24/09/2010 |