Machine Learning
Boosting with structure information in the functional space: an application to graph classification
Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining
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With increasing number of chemicals produced each year, it still remains a daunting task to keep up with the toxicity profile of each chemical. In this paper, we attempt to predict toxicity of compounds using computational techniques, where results from certain in vitro assays applied on 309 chemicals, along with computed properties of chemicals are used to predict the toxicity caused by them at a particular endpoint. We show that both Random Forest RF and Naïve Bayes NB have a good performance. We also show that using small and related trees in RF helps to further improve the performance.