Domain-invariant features for mechanism of action prediction in a multi-cell-line drug screen
Author(s) -
Joseph Boyd,
Alice Pinheiro,
Elaine Del Nery,
Fabien Reyal,
Thomas Walter
Publication year - 2019
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/btz774
Subject(s) - computer science , pipeline (software) , drug discovery , benchmark (surveying) , feature (linguistics) , computational biology , invariant (physics) , artificial intelligence , bioinformatics , biology , mathematics , mathematical physics , linguistics , philosophy , geodesy , programming language , geography
High-content screening is an important tool in drug discovery and characterization. Often, high-content drug screens are performed on one single-cell line. Yet, a single-cell line cannot be thought of as a perfect disease model. Many diseases feature an important molecular heterogeneity. Consequently, a drug may be effective against one molecular subtype of a disease, but less so against another. To characterize drugs with respect to their effect not only on one cell line but on a panel of cell lines is therefore a promising strategy to streamline the drug discovery process.
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