Multi-task Drug Bioactivity Classification with Graph Labeling Ensembles
We present a new method for drug bioactivity classification based on learning an ensemble of multi-task classifiers. As the base classifiers of the ensemble we use Max-Margin Conditional Random Field (MMCRF) models, which have previously obtained the state-of-the-art accuracy in this problem. MMCRF relies on a graph structure coupling the set of tasks together, and thus turns the multi-task learning problem into a graph labeling problem. In our ensemble method the graphs of the base classifiers are random, constructed by random pairing or random spanning tree extraction over the set of tasks.
We compare the ensemble approaches on datasets containing the cancer inhibition potential of drug-like molecules against 60 cancer cell lines. In our experiments we find that ensembles based on random graphs surpass the accuracy of single SVM as well as a single MMCRF model relying on a graph built from auxiliary data.
Keywordsdrug bioactivity prediction multi-task learning ensemble methods kernel methods
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