Looking at the BiG picture: incorporating bipartite graphs in drug response prediction
Author(s) -
David Earl Hostallero,
Yihui Li,
Amin Emad
Publication year - 2022
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/btac383
Subject(s) - bipartite graph , drug response , computer science , leverage (statistics) , pharmacogenomics , python (programming language) , drug , graph , big data , machine learning , artificial intelligence , computational biology , theoretical computer science , data mining , bioinformatics , biology , pharmacology , operating system
The increasing number of publicly available databases containing drugs' chemical structures, their response in cell lines, and molecular profiles of the cell lines has garnered attention to the problem of drug response prediction. However, many existing methods do not fully leverage the information that is shared among cell lines and drugs with similar structure. As such, drug similarities in terms of cell line responses and chemical structures could prove to be useful in forming drug representations to improve drug response prediction accuracy.
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