Using the Encoder Embedded Framework of Dimensionality Reduction Based on Multiple Drugs Properties for Drug Recommendation

  • Jun Ma
  • Ruisheng ZhangEmail author
  • Rongjing Hu
  • Yong Mu
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10942)


After obtaining a large amount of drug information, how to extract the most important features from various high-dimensional attribute datasets for drug recommendation has become an important task in the initial stage of drug repositioning. Dimensionality reduction is a necessary and important task for getting the best features in next step. In this paper, three important attribute data about the drugs (i.e., chemical structures, target proteins and side effects) are selected, and two deep frameworks named as F1 and F2 are used to accomplish the task of dimensionality reduction. The processed data are used for recommending new indications by collaborative filtering algorithm. In order to compare the results, two important values of Mean Absolute Error (MAE) and Coverage are selected to evaluate the performance of the two frameworks. Through the comparison with the results of Principal Components Analysis (PCA), it shows that the two deep frameworks proposed in this paper perform better than PCA and can be used for dimensionality reduction task in the future in drug repositioning.


Machine learning Autocoder Encoder Dimensionality reduction PCA Drug recommendation 



This work was supported by the Fundamental Research Funds for the Central Universities (No. lzujbky-2017-195).


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© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  1. 1.School of Information Science and EngineeringLanzhou UniversityLanzhouChina

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