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Machine Learning Approach for Predicting New Uses of Existing Drugs and Evaluation of Their Reliabilities
1Kogakuin University, Tokyo, Japan. fukuoka@cc.kogakuin.ac.jp.
Abstract:
In this chapter, a new method to evaluate the reliability of predicting new uses of existing drugs was proposed. The prediction was performed with a support vector machine (SVM) using various data. Because the reliability of prediction could not be evaluated based on the output of an SVM, which was binary, the proposed method evaluated the reliability as a product of a distance from the separating hyperplane of the SVM and a similarity between the disease targeted by the drug and a candidate disease. A validation using real data revealed that the performance of the proposed method was promising.
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However, the point estimate is most likely not the exact value of the population parameter, but close to it. After calculating point estimates, we construct interval estimates, called confidence intervals or prediction intervals. This prediction interval comprises a range of values unlike the point estimate and is a better predictor of the observed sample value, y.