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Bipartite graph-based approach for clustering of cell lines by gene expression–drug response associations
Author(s) -
Calvin Chi,
Yuting Ye,
Bin Chen,
Haiyan Huang
Publication year - 2021
Publication title -
bioinformatics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 3.599
H-Index - 390
eISSN - 1367-4811
pISSN - 1367-4803
DOI - 10.1093/bioinformatics/btab143
Subject(s) - cluster analysis , computational biology , bipartite graph , pharmacogenomics , context (archaeology) , biology , computer science , graph , bioinformatics , artificial intelligence , theoretical computer science , paleontology
In pharmacogenomic studies, the biological context of cell lines influences the predictive ability of drug-response models and the discovery of biomarkers. Thus, similar cell lines are often studied together based on prior knowledge of biological annotations. However, this selection approach is not scalable with the number of annotations, and the relationship between gene-drug association patterns and biological context may not be obvious.

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