TY - JOUR
T1 - Systems Pharmacogenomic Landscape of Drug Similarities from LINCS data
T2 - Drug Association Networks
AU - Musa, Aliyu
AU - Tripathi, Shailesh
AU - Dehmer, Matthias
AU - Yli-Harja, Olli
AU - Kauffman, Stuart A.
AU - Emmert-Streib, Frank
PY - 2019/12/1
Y1 - 2019/12/1
N2 - Modern research in the biomedical sciences is data-driven utilizing high-throughput technologies to generate big genomic data. The Library of Integrated Network-based Cellular Signatures (LINCS) is an example for a large-scale genomic data repository providing hundred thousands of high-dimensional gene expression measurements for thousands of drugs and dozens of cell lines. However, the remaining challenge is how to use these data effectively for pharmacogenomics. In this paper, we use LINCS data to construct drug association networks (DANs) representing the relationships between drugs. By using the Anatomical Therapeutic Chemical (ATC) classification of drugs we demonstrate that the DANs represent a systems pharmacogenomic landscape of drugs summarizing the entire LINCS repository on a genomic scale meaningfully. Here we identify the modules of the DANs as therapeutic attractors of the ATC drug classes.
AB - Modern research in the biomedical sciences is data-driven utilizing high-throughput technologies to generate big genomic data. The Library of Integrated Network-based Cellular Signatures (LINCS) is an example for a large-scale genomic data repository providing hundred thousands of high-dimensional gene expression measurements for thousands of drugs and dozens of cell lines. However, the remaining challenge is how to use these data effectively for pharmacogenomics. In this paper, we use LINCS data to construct drug association networks (DANs) representing the relationships between drugs. By using the Anatomical Therapeutic Chemical (ATC) classification of drugs we demonstrate that the DANs represent a systems pharmacogenomic landscape of drugs summarizing the entire LINCS repository on a genomic scale meaningfully. Here we identify the modules of the DANs as therapeutic attractors of the ATC drug classes.
KW - Data Science/methods
KW - Databases, Genetic/statistics & numerical data
KW - Databases, Pharmaceutical/statistics & numerical data
KW - Gene Expression Profiling
KW - Gene Regulatory Networks
KW - Humans
KW - Pharmacogenetics/methods
KW - Pharmacological Phenomena/genetics
KW - Systems Biology/methods
UR - http://www.scopus.com/inward/record.url?scp=85066962993&partnerID=8YFLogxK
U2 - 10.1038/s41598-019-44291-3
DO - 10.1038/s41598-019-44291-3
M3 - Article
C2 - 31127155
AN - SCOPUS:85066962993
SN - 2045-2322
VL - 9
SP - 7849
JO - Scientific Reports
JF - Scientific Reports
IS - 1
M1 - 7849
ER -