Small Molecules' Multi-Metabolic Pathways Prediction Using Physico- Chemical Features and Multi-Task Learning Method.
Knowledge of mechanism of small molecules in metabolic pathway is critical to design specific and effective
inhibitors for metabolic pathway. As some small molecules are involved in more than one pathway, it is crucial to use an
accurate and robust approach to correctly map the small molecule in specific metabolic pathway that it is involved in. In
this article, small molecules are studied using the Minimal-Redundancy-Maximal-Relevance-Forward Feature Search
(mRMR-FFS) method combined with Multi-task learning method based on K-nearest neighbor (KNN) Algorithms
method. Forty-five important chemical features were found based on 10-folds cross validation test from original data set
containing 61 features. By applying KNN method with these forty-five selected features, the accuracy rate of prediction
model could achieve 68.2% for the 10-folds cross validation test. It is promosing that our two stage scheme can be a
useful approach for searching new effective competitive drugs in metabolic pathway.
Keywords: KEGG, KNN, metabolic pathway, mRMR, multi-task learning, small molecules.
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