Drug-target protein interaction prediction based on AdaBoost algorithm / 生物医学工程学杂志

The drug-target protein interaction prediction can be used for the discovery of new drug effects. Recent studies often focus on the prediction of an independent matrix filling algorithm, which apply a single algorithm to predict the drug-target protein interaction. The single-model matrix-filling algorithms have low accuracy, so it is difficult to obtain satisfactory results in the prediction of drug-target protein interaction. AdaBoost algorithm is a strong multiple classifier combination framework, which is proved by the past researches in classification applications. The drug-target interaction prediction is a matrix filling problem. Therefore, we need to adjust the matrix filling problem to a classification problem before predicting the interaction among drug-target protein. We make full use of the AdaBoost algorithm framework to integrate several weak classifiers to improve performance and make accurate prediction of drug-target protein interaction. Experimental results based on the metric datasets show that our algorithm outperforms the other state-of-the-art approaches and classical methods in accuracy. Our algorithm can overcome the limitations of the single algorithm based on machine learning method, exploit the hidden factors better and improve the accuracy of prediction effectively.

A Comparative Study of Undergraduate Examinations to Evaluate Medical Students' Performance and the National Examination for Medical Practitioners / 医学教育

To demonstrate the quality assurance of the comprehensive examination of sixth-year students at Nippon Medical School, 4 undergraduate examinations were compared with the national examination for medical practitioners (NEMP) using scatter graphs and Pearson's correlation coefficient. Of the 93 sixth-year students at Nippon Medical School, 57%(n=53) reported their scores on the NEMP in response to a request from the Academic Quality and Development Office. Correlation coefficients of the grade point average (years 1 to 5), average scores on graduation examinations of 24 subjects, scores on the trial examination of NEMP, and scores on the sixth-year comprehensive examination with overall scores on the NEMP were 0.62, 0.46, 0.68, and 0.63, respectively. These results suggest that the sixth-year comprehensive examination is more suitable than are graduation examinations for predicting the NEMP score.