Open AccessNetwork-based screen in iPSC-derived cells reveals therapeutic candidate for heart valve disease
Author(s) -
Christina V. Theodoris,
Ping Zhou,
Lei Liu,
Yu Zhang,
Tomohiro Nishino,
Yu Huang,
Aleksandra Kostina,
Sanjeev S. Ranade,
Casey A. Gifford,
Vladimir Uspenskiy,
Anna Malashicheva,
Sheng Ding,
Deepak Srivastava
Publication year - 2020
Publication title -
science
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 12.556
H-Index - 1186
eISSN - 1095-9203
pISSN - 0036-8075
DOI - 10.1126/science.abd0724
Subject(s) - induced pluripotent stem cell , drug discovery , disease , mechanism (biology) , gene regulatory network , heart disease , biological network , computational biology , computer science , bioinformatics , medicine , biology , gene , gene expression , pathology , biochemistry , philosophy , embryonic stem cell , epistemology
Machine learning for medicine Small-molecule screens aimed at identifying therapeutic candidates traditionally search for molecules that affect one to several outputs at most, limiting discovery of true disease-modifying drugs. Theodoriset al. developed a machine-learning approach to identify small molecules that broadly correct gene networks dysregulated in a human induced pluripotent stem cell disease model of a common form of heart disease involving the aortic valve. Gene network correction by the most efficacious therapeutic candidate generalized to primary aortic valve cells derived from more than 20 patients with sporadic aortic valve disease and prevented aortic valve disease in vivo in a mouse model.Science , this issue p.eabd0724
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