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Multi-target drug repositioning by bipartite block-wise sparse multi-task learning.

Limin Li1, Xiao He2,3, Karsten Borgwardt4,5

  • 1School of Mathematics and Statistics, Xi'an Jiaotong University, Xi'an, 710049, China.

BMC Systems Biology
|May 11, 2018
PubMed
Summary
This summary is machine-generated.

A new algorithm, BBSS-MTL, identifies multiple drug targets by analyzing gene expression profiles. This method improves upon existing techniques for drug repositioning and target identification.

Keywords:
Drug repositioningL1000Multi-task learning

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Area of Science:

  • Computational biology
  • Bioinformatics
  • Genomics

Background:

  • Drug target identification is essential for drug discovery.
  • The LINCS L1000 database offers gene expression data for analyzing compound-target relationships.
  • Current methods like connectivity mapping assume single-target drug effects, ignoring multi-target interactions.

Purpose of the Study:

  • To develop a novel algorithm for identifying sets of gene knock-downs that mimic drug-induced gene expression changes.
  • To improve multi-target drug repositioning by overcoming limitations of existing analyses.

Main Methods:

  • Proposed a bipartite block-wise sparse multi-task learning model with super-graph structure (BBSS-MTL).
  • Assumed additive effects of gene knock-downs.
  • Applied the model to analyze gene expression profiles from the LINCS L1000 database.

Main Results:

  • The BBSS-MTL model accurately predicts potential drug targets.
  • Demonstrated improved accuracy compared to pairwise connectivity mapping analysis.
  • Validated on five distinct cancer cell line datasets.

Conclusions:

  • BBSS-MTL offers a more accurate approach for predicting drug targets.
  • The method enhances multi-target drug repositioning capabilities.
  • The algorithm effectively addresses the limitations of single-target assumptions in drug effect analysis.