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Predicting drug-induced transcriptome responses of a wide range of human cell lines by a novel tensor-train

Michio Iwata1, Longhao Yuan2,3, Qibin Zhao3,4

  • 1Department of Bioscience and Bioinformatics, Faculty of Computer Science and Systems Engineering, Kyushu Institute of Technology, Iizuka, Fukuoka, Japan.

Bioinformatics (Oxford, England)
|September 13, 2019
PubMed
Abstract

Insights

This study introduces a new computational method to predict gene expression profiles for drug treatments in human cell lines. This approach enhances drug repositioning accuracy and identifies novel therapeutic indications for diseases.

Area of Science:

  • Computational biology
  • Genomics
  • Pharmacology

Background:

  • Understanding genome-wide transcriptomic responses to drug treatments is crucial but challenging due to incomplete gene expression data.
  • Existing data lacks comprehensive profiles for all drug-cell line combinations, hindering practical applications in medicine and pharmaceuticals.

Purpose of the Study:

  • To develop a novel computational method for predicting missing drug-induced gene expression profiles.
  • To predict new therapeutic indications for diseases using computational modeling.
  • To improve the accuracy of drug repositioning.

Main Methods:

  • Developed a tensor-train weighted optimization (TT-WOPT) algorithm to predict unknown values in tensor-structured gene expression data.
  • Applied the TT-WOPT algorithm to reconstruct drug-induced gene expression data for human cell lines.
  • Evaluated the impact of imputed gene expression profiles on drug repositioning accuracy.

Main Results:

  • The TT-WOPT algorithm accurately reconstructed drug-induced gene expression data across various human cell lines.
  • Imputed gene expression profiles significantly improved the accuracy of drug repositioning compared to original profiles.
  • Comprehensive predictions identified numerous potential drug indications for diseases missed by previous methods.

Conclusions:

  • The developed TT-WOPT method provides an effective approach for predicting gene expression profiles and uncovering novel drug indications.
  • This computational strategy advances drug discovery and personalized medicine by leveraging incomplete biological data.
  • The findings suggest a powerful new tool for pharmaceutical research and disease treatment strategies.

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