Abstract
A machine learning-based approach to the prediction of molecular bioactivity in new drugs is proposed. Two important aspects are considered for the task: feature subset selection and cost-sensitive classification. These are to cope with the huge number of features and unbalanced samples in a dataset of drug candidates. We designed a pattern classifier with such capabilities based on information theory and re-sampling techniques. Experimental results demonstrate the feasibility of the proposed approach. In particular, the classification accuracy of our approach was higher than that of the winner of KDD Cup 2001 competition.
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Lee, S., Yang, J., Oh, Kw. (2003). Prediction of Molecular Bioactivity for Drug Design Using a Decision Tree Algorithm. In: Grieser, G., Tanaka, Y., Yamamoto, A. (eds) Discovery Science. DS 2003. Lecture Notes in Computer Science(), vol 2843. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-39644-4_32
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DOI: https://doi.org/10.1007/978-3-540-39644-4_32
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-20293-6
Online ISBN: 978-3-540-39644-4
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