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Predicting drug–disease associations by network embedding and biomedical data integration

Published on Apr 1, 2019in Drug Testing and Analysis2.80
· DOI :10.1108/dta-01-2019-0004
Xiaomei Wei1
Estimated H-index: 1
(HAU: Huazhong Agricultural University),
Yaliang Zhang (HAU: Huazhong Agricultural University)+ 1 AuthorsYaping Fang1
Estimated H-index: 1
(HAU: Huazhong Agricultural University)
Abstract
The traditional drug development process is costly, time consuming and risky. Using computational methods to discover drug repositioning opportunities is a promising and efficient strategy in the era of big data. The explosive growth of large-scale genomic, phenotypic data and all kinds of “omics” data brings opportunities for developing new computational drug repositioning methods based on big data. The paper aims to discuss this issue.,Here, a new computational strategy is proposed for inferring drug–disease associations from rich biomedical resources toward drug repositioning. First, the network embedding (NE) algorithm is adopted to learn the latent feature representation of drugs from multiple biomedical resources. Furthermore, on the basis of the latent vectors of drugs from the NE module, a binary support vector machine classifier is trained to divide unknown drug–disease pairs into positive and negative instances. Finally, this model is validated on a well-established drug–disease association data set with tenfold cross-validation.,This model obtains the performance of an area under the receiver operating characteristic curve of 90.3 percent, which is comparable to those of similar systems. The authors also analyze the performance of the model and validate its effect on predicting the new indications of old drugs.,This study shows that the authors’ method is predictive, identifying novel drug–disease interactions for drug discovery. The new feature learning methods also positively contribute to the heterogeneous data integration.
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References37
Newest
#1Peng Cui (THU: Tsinghua University)H-Index: 27
#2Xiao Wang (THU: Tsinghua University)H-Index: 32
Last.Wenwu Zhu (THU: Tsinghua University)H-Index: 46
view all 4 authors...
#1Wen Zhang (WHU: Wuhan University)H-Index: 17
#2Xiang Yue (WHU: Wuhan University)H-Index: 6
Last.Feng Liu (WHU: Wuhan University)H-Index: 7
view all 7 authors...
#1Sanja Krakan (University of Zagreb)H-Index: 1
#2Luka Humski (University of Zagreb)H-Index: 4
Last.Zoran Skočir (University of Zagreb)H-Index: 8
view all 3 authors...
Aug 13, 2016 in KDD (Knowledge Discovery and Data Mining)
#1Aditya Grover (Stanford University)H-Index: 8
#2Jure Leskovec (Stanford University)H-Index: 86
#1Michael Kuhn (MPG: Max Planck Society)H-Index: 29
#2Ivica LetunicH-Index: 48
Last.Peer Bork (Molecular Medicine Partnership Unit)H-Index: 178
view all 4 authors...
May 18, 2015 in WWW (The Web Conference)
#1Jian Tang (Microsoft)H-Index: 15
#2Meng Qu (PKU: Peking University)H-Index: 8
Last.Qiaozhu Mei (UM: University of Michigan)H-Index: 38
view all 6 authors...
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