Predicting microRNA–disease associations from lncRNA–microRNA interactions via Multiview Multitask Learning

Research output: Journal Publications and Reviews (RGC: 21, 22, 62)21_Publication in refereed journalpeer-review

11 Scopus Citations
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Author(s)

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

Related Research Unit(s)

Detail(s)

Original languageEnglish
Article numberbbaa133
Journal / PublicationBriefings in Bioinformatics
Volume22
Issue number3
Online published7 Jul 2020
Publication statusPublished - May 2021

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 https://github.com/yahuang1991polyu/MVMTMDA/.

Research Area(s)

  • microRNA-disease association, lncRNA–microRNA interaction, multiview multitask learning

Bibliographic Note

Research Unit(s) information for this publication is provided by the author(s) concerned.

Citation Format(s)

Predicting microRNA–disease associations from lncRNA–microRNA interactions via Multiview Multitask Learning. / Huang, Yu-An; Chan, Keith C. C.; You, Zhu-Hong; Hu, Pengwei; Wang, Lei; Huang, Zhi-An.

In: Briefings in Bioinformatics, Vol. 22, No. 3, bbaa133, 05.2021.

Research output: Journal Publications and Reviews (RGC: 21, 22, 62)21_Publication in refereed journalpeer-review