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Prediction of Potential Drug–Disease Associations through Deep Integration of Diversity and Projections of Various Drug Features

Published on Aug 22, 2019in International Journal of Molecular Sciences4.18
· DOI :10.3390/ijms20174102
Ping Xuan3
Estimated H-index: 3
,
Yingying Song + 1 AuthorsLan Jia
Abstract
Identifying new indications for existing drugs may reduce costs and expedites drug development. Drug-related disease predictions typically combined heterogeneous drug-related and disease-related data to derive the associations between drugs and diseases, while recently developed approaches integrate multiple kinds of drug features, but fail to take the diversity implied by these features into account. We developed a method based on non-negative matrix factorization, DivePred, for predicting potential drug–disease associations. DivePred integrated disease similarity, drug–disease associations, and various drug features derived from drug chemical substructures, drug target protein domains, drug target annotations, and drug-related diseases. Diverse drug features reflect the characteristics of drugs from different perspectives, and utilizing the diversity of multiple kinds of features is critical for association prediction. The various drug features had higher dimensions and sparse characteristics, whereas DivePred projected high-dimensional drug features into the low-dimensional feature space to generate dense feature representations of drugs. Furthermore, DivePred’s optimization term enhanced diversity and reduced redundancy of multiple kinds of drug features. The neighbor information was exploited to infer the likelihood of drug–disease associations. Experiments indicated that DivePred was superior to several state-of-the-art methods for prediction drug-disease association. During the validation process, DivePred identified more drug-disease associations in the top part of prediction result than other methods, benefitting further biological validation. Case studies of acetaminophen, ciprofloxacin, doxorubicin, hydrocortisone, and ampicillin demonstrated that DivePred has the ability to discover potential candidate disease indications for drugs.
  • References (36)
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References36
Newest
#1Ping Xuan (Heilongjiang University)H-Index: 3
#2Yangkun Cao (Heilongjiang University)H-Index: 2
Last.Tonghui Shen (Heilongjiang University)H-Index: 3
view all 6 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...
#1Maryam Lotfi Shahreza (IUT: Isfahan University of Technology)H-Index: 2
#2Nasser Ghadiri (IUT: Isfahan University of Technology)H-Index: 7
Last.James R. Green (IUT: Isfahan University of Technology)H-Index: 15
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#1Jing Wang (BU: Bournemouth University)H-Index: 6
#2Feng Tian (BU: Bournemouth University)H-Index: 15
Last.Xiao Wang (THU: Tsinghua University)H-Index: 32
view all 6 authors...
#1Yan Zhao (CUMT: China University of Mining and Technology)H-Index: 2
#2Xing Chen (CUMT: China University of Mining and Technology)H-Index: 15
Last.Jun Yin (CUMT: China University of Mining and Technology)H-Index: 4
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#1Azam Peyvandipour (WSU: Wayne State University)H-Index: 2
#2Nafiseh Saberian (WSU: Wayne State University)H-Index: 2
Last.Sorin Draghici (WSU: Wayne State University)H-Index: 38
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#1Michael S. Kinch (WashU: Washington University in St. Louis)H-Index: 11
#2Rebekah H. Griesenauer (WashU: Washington University in St. Louis)H-Index: 3
#1Xing Chen (CUMT: China University of Mining and Technology)H-Index: 31
#2Lei Wang (CUMT: China University of Mining and Technology)H-Index: 5
Last.Jianqiang Li (SZU: Shenzhen University)H-Index: 22
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