Predicting microRNA-disease associations using label propagation based on linear neighborhood similarity

Journal of Biomedical Informatics
Guanghui LiPingjian Ding

Abstract

Interactions between microRNAs (miRNAs) and diseases can yield important information for uncovering novel prognostic markers. Since experimental determination of disease-miRNA associations is time-consuming and costly, attention has been given to designing efficient and robust computational techniques for identifying undiscovered interactions. In this study, we present a label propagation model with linear neighborhood similarity, called LPLNS, to predict unobserved miRNA-disease associations. Additionally, a preprocessing step is performed to derive new interaction likelihood profiles that will contribute to the prediction since new miRNAs and diseases lack known associations. Our results demonstrate that the LPLNS model based on the known disease-miRNA associations could achieve impressive performance with an AUC of 0.9034. Furthermore, we observed that the LPLNS model based on new interaction likelihood profiles could improve the performance to an AUC of 0.9127. This was better than other comparable methods. In addition, case studies also demonstrated our method's outstanding performance for inferring undiscovered interactions between miRNAs and diseases, especially for novel diseases.

Citations

Apr 8, 2020·Frontiers in Bioengineering and Biotechnology·Fuxing LiuLiqian Zhou
Aug 28, 2020·Frontiers in Bioengineering and Biotechnology·Yongxian FanWanru Wang
Aug 18, 2020·Methods : a Companion to Methods in Enzymology·Yulian DingFang-Xiang Wu
Dec 21, 2019·Journal of Biomedical Informatics·Jihwan HaSanghyun Park
Nov 6, 2020·Cancer Management and Research·Xiang-Shu XianXiao-Meng Jiang
Nov 6, 2020·Journal of Bioinformatics and Computational Biology·Ahmet Toprak, Esma Eryilmaz
Mar 30, 2021·Briefings in Bioinformatics·Jingru WangQing Li
Aug 21, 2021·Artificial Intelligence in Medicine·Jin LiYun Yang
Nov 17, 2021·IET Systems Biology·Wei WangXianfang Wang
Nov 21, 2020·Journal of Biomedical Informatics·Guanghui LiHailin Chen

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