EMUDRA: Ensemble of Multiple Drug Repositioning Approaches to improve prediction accuracy
Author(s) -
Xianxiao Zhou,
Minghui Wang,
Igor Katsyv,
Hanna Y. Irie,
Bin Zhang
Publication year - 2018
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/bty325
Subject(s) - computer science , drug repositioning , machine learning , artificial intelligence , data mining , statistic , drug , medicine , mathematics , statistics , psychiatry
Availability of large-scale genomic, epigenetic and proteomic data in complex diseases makes it possible to objectively and comprehensively identify the therapeutic targets that can lead to new therapies. The Connectivity Map has been widely used to explore novel indications of existing drugs. However, the prediction accuracy of the existing methods, such as Kolmogorov-Smirnov statistic remains low. Here we present a novel high-performance drug repositioning approach that improves over the state-of-the-art methods.
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