Autoencoder Based Feature Selection Method for Classification of Anticancer Drug Response

Anticancer drug responses can be varied for individual patients. This difference is mainly caused by genetic reasons, like mutations and RNA expression. Thus, these genetic features are often used to construct classification models to predict the drug response. This research focuses on the feature selection issue for the classification models. Because of the vast dimensions of the feature space for predicting drug response, the autoencoder network was first built, and a subset of inputs with the important contribution was selected. Then by using the Boruta algorithm, a further small set of features was determined for the random forest, which was used to predict drug response. Two datasets, GDSC and CCLE, were used to illustrate the efficiency of the proposed method.

• Publication year

  2019

• Venue

  Frontiers in Genetics

• Publication date

  2019-03-27

• Fields of study

  Medicine, Computer Science

• Identifiers
  DOI 10.3389/fgene.2019.00233PMID 30972101PMCID 6445890
• External record

  Open on Semantic Scholar

• Source metadata

  Semantic Scholar, PubMed

• No claims are published for this paper.

• No concepts are published for this paper.

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