title: Drug similarity search based on combined signatures in gene expression profiles.
abstract: OBJECTIVE
  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.
authors.affiliation: Department of Bio and Brain Engineering, Korea Advanced Institute of Science and Technology, Daejeon, Korea.
authors.forename: Kihoon
authors.initials: K
authors.lastname: Cha
authors.affiliation: Department of Information and Communications Engineering, Korea Advanced Institute of Science and Technology, Daejeon, Korea.
authors.forename: Min-Sung
authors.initials: MS
authors.lastname: Kim
authors.affiliation: Department of Bio and Brain Engineering, Korea Advanced Institute of Science and Technology, Daejeon, Korea.
authors.forename: Kimin
authors.initials: K
authors.lastname: Oh
authors.affiliation: Department of Industrial Engineering, Ajou University, Suwon, Korea.
authors.forename: Hyunjung
authors.initials: H
authors.lastname: Shin
authors.affiliation: Department of Bio and Brain Engineering, Korea Advanced Institute of Science and Technology, Daejeon, Korea.
authors.forename: Gwan-Su
authors.initials: GS
authors.lastname: Yi
keywords: Drug Repositioning; Gene Expression Regulation; Gene Regulatory Networks; Pharmacological Biomarkers; Transcriptome