multiview representation
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2022 ◽  
pp. 1-14
Xiaoli Sun ◽  
Xiujun Zhang ◽  
Chen Xu ◽  
Mingqing Xiao ◽  
Yuanyan Tang

2021 ◽  
Ryan Dew ◽  
Asim Ansari ◽  
Olivier Toubia

The authors develop a decision support system for design and branding based on a multimodal variational autoencoder that merges image, text, and ratings data.

Yu-An Huang ◽  
Keith C C Chan ◽  
Zhu-Hong You ◽  
Pengwei Hu ◽  
Lei Wang ◽  

Abstract Motivation Identifying microRNAs that are associated with different diseases as biomarkers is a problem of great medical significance. Existing computational methods for uncovering such microRNA-diseases associations (MDAs) are mostly developed under the assumption that similar microRNAs tend to associate with similar diseases. Since such an assumption is not always valid, these methods may not always be applicable to all kinds of MDAs. Considering that the relationship between long noncoding RNA (lncRNA) and different diseases and the co-regulation relationships between the biological functions of lncRNA and microRNA have been established, we propose here a multiview multitask method to make use of the known lncRNA–microRNA interaction to predict MDAs on a large scale. The investigation is performed in the absence of complete information of microRNAs and any similarity measurement for it and to the best knowledge, the work represents the first ever attempt to discover MDAs based on lncRNA–microRNA interactions. Results In this paper, we propose to develop a deep learning model called MVMTMDA that can create a multiview representation of microRNAs. The model is trained based on an end-to-end multitasking approach to machine learning so that, based on it, missing data in the side information can be determined automatically. Experimental results show that the proposed model yields an average area under ROC curve of 0.8410+/−0.018, 0.8512+/−0.012 and 0.8521+/−0.008 when k is set to 2, 5 and 10, respectively. In addition, we also propose here a statistical approach to predicting lncRNA-disease associations based on these associations and the MDA discovered using MVMTMDA. Availability Python code and the datasets used in our studies are made available at

IEEE Access ◽  
2019 ◽  
Vol 7 ◽  
pp. 120670-120680 ◽  
Tianhuan Huang ◽  
Lei Chen ◽  
Yuncong Feng ◽  
Xianye Ben ◽  
Ruixue Xiao ◽  

2015 ◽  
Vol 23 (8) ◽  
pp. 1295-1308 ◽  
Mohamed Morchid ◽  
Mohamed Bouallegue ◽  
Richard Dufour ◽  
Georges Linares ◽  
Driss Matrouf ◽  

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