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Drug Effect Prediction by Integrating L1000 Genomic and Proteomic Big Data

  • Wei Chen
  • Xiaobo ZhouEmail author
Protocol
Part of the Methods in Molecular Biology book series (MIMB, volume 1939)

Abstract

The library of integrated Network-Based Cellular Signatures (LINCS) project aims to create a network-based understanding of biology by cataloging changes in gene expression and signal transduction. Gene expression and proteomic data in LINCS L1000 are cataloged for human cancer cells treated with compounds and genetic reagents. For understanding the related cell pathways and facilitating drug discovery, we developed binary linear programming (BLP) to infer cell-specific pathways and identify compounds’ effects using L1000 gene expression and phosphoproteomics data. A generic pathway map for the MCF7 breast cancer cell line was built. Within them, BLP extracted the cell-specific pathways, which reliably predicted the compounds’ effects. In this way, the potential drug effects are revealed by our models.

Key words

LINCS L1000 Binary linear programming Drug effect Cell-specific pathway 

Notes

Acknowledgment

The work was supported by the grants of NIH U01HL111560-04 (Zhou) and NIH U01CA166886-03 (Zhou).

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Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Department of RadiologyWake Forest University Medical SchoolWinston-SalemUSA
  2. 2.School of Biomedical InformaticsThe University of Texas, Health Science Center at HoustonHoustonUSA

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