Association of feature gene expression with structural fingerprints of chemical compounds

Yun Li, Kang Tu, Siyuan Zheng, Jingfang Wang, Yixue Li, Pei Hao, Xuan Li

Research output: Contribution to journalArticlepeer-review

4 Scopus citations

Abstract

Exploring the relationship between a chemical structure and its biological function is of great importance for drug discovery. For understanding the mechanisms of drug action, researchers traditionally focused on the molecular structures in the context of interactions with targets. The newly emerged high-throughput "omics" technology opened a new dimension to study the structurefunction relationship of chemicals. Previous studies made attempts to introduce transcriptomics data into chemical function investigation. But little effort has been made to link structural fingerprints of compounds with defined intracellular functions, i.e. expression of particular genes and altered pathways. By integrating the chemical structural information with the gene expression profiles of chemical-treated cells, we developed a novel method to associate the structural difference between compounds with the expression of a definite set of genes, which were called feature genes. A subtraction protocol was designed to extract a minimum gene set related to chemical structural features, which can be utilized in practice as markers for drug screening. Case studies demonstrated that our approach is capable of finding feature genes associated with chemical structural fingerprints.

Original languageEnglish (US)
Pages (from-to)503-519
Number of pages17
JournalJournal of bioinformatics and computational biology
Volume9
Issue number4
DOIs
StatePublished - Aug 1 2011
Externally publishedYes

Keywords

  • Feature genes
  • chemical structure
  • machine learning
  • similarity

ASJC Scopus subject areas

  • Biochemistry
  • Molecular Biology
  • Computer Science Applications

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