Graph Kernels Based on Relevant Patterns and Cycle Information for Chemoinformatics
Résumé
Chemoinformatics aim to predict molecule's prop- erties through informational methods. Computer sci- ence's research fields concerned with chemoinformat- ics are machine learning and graph theory. From this point of view, graph kernels provide a nice framework for combining these two fields. We present in this paper two contributions to this research field: a graph kernel based on an optimal linear combination of kernels ap- plied to acyclic patterns and a new kernel on the cyclic system of two graphs. These two extensions are vali- dated on two chemoinformatics datasets.
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