Healthc Inform Res.  2014 Jan;20(1):52-60. 10.4258/hir.2014.20.1.52.

Drug Similarity Search Based on Combined Signatures in Gene Expression Profiles

Affiliations
  • 1Department of Bio and Brain Engineering, Korea Advanced Institute of Science and Technology, Daejeon, Korea. gsyi@kaist.ac.kr
  • 2Department of Information and Communications Engineering, Korea Advanced Institute of Science and Technology, Daejeon, Korea.
  • 3Department of Industrial Engineering, Ajou University, Suwon, Korea.

Abstract


OBJECTIVES
Recently, comparison of drug responses on gene expression has been a major approach to identifying the functional similarity of drugs. Previous studies have mostly focused on a single feature, the expression differences of individual genes. We provide a more robust and accurate method to compare the functional similarity of drugs by diversifying the features of comparison in gene expression and considering the sample dependent variations.
METHODS
For differentially expressed gene measurement, we modified the conventional t-test to normalize variations in diverse experimental conditions of individual samples. To extract significant differentially co-expressed gene modules, we searched maximal cliques among the co-expressed gene network. Finally, we calculated a combined similarity score by averaging the two scaled scores from the above two measurements.
RESULTS
This method shows significant performance improvement in comparison to other approaches in the test with Connectivity Map data. In the test to find the drugs based on their own expression profiles with leave-one-out cross validation, the proposed method showed an area under the curve (AUC) score of 0.99, which is much higher than scores obtained with previous methods, ranging from 0.71 to 0.93. In the drug networks, we could find well clustered drugs having the same target proteins and novel relations among drugs implying the possibility of drug repurposing.
CONCLUSIONS
Inclusion of the features of a co-expressed module provides more implications to infer drug action. We propose that this method be used to find collaborative cellular mechanisms associated with drug action and to simply identify drugs having similar responses.

Keyword

Pharmacological Biomarkers; Transcriptome; Gene Expression Regulation; Gene Regulatory Networks; Drug Repositioning

MeSH Terms

Biomarkers, Pharmacological
Drug Repositioning
Gene Expression Regulation
Gene Expression*
Gene Regulatory Networks
Methods
Transcriptome*
Biomarkers, Pharmacological

Figure

  • Figure 1 Overall procedures to measure differential expression similarity. (A) identification of differentially co-expressed gene module signature. (B) identification of differentially expressed gene signature. (C) Calculation combined score of the two different signatures. FDR: false discovery rate.

  • Figure 2 Predicted drug-drug network of selected 29 drugs.


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