Journal of Biomedical Informatics 82 (2018) 169–177 Contents lists available at ScienceDirect Journal of Biomedical Informatics journal homepage: www.elsevier.com/locate/yjbin Predicting microRNA-disease associations using label propagation based on linear neighborhood similarity a, b b c b Guanghui Li , Jiawei Luo , Qiu Xiao , Cheng Liang , Pingjian Ding School of Information Engineering, East China Jiaotong University, Nanchang, China College of Computer Science and Electronic Engineering, Hunan University, Changsha, China College of Information Science and Engineering, Shandong Normal University, Jinan, China ARTIC L E I NF O ABSTRAC T Keywords: Interactions between microRNAs (miRNAs) and diseases can yield important information for uncovering novel Disease-related miRNAs prognostic markers. Since experimental determination of disease-miRNA associations is time-consuming and miRNA-disease association costly, attention has been given to designing eﬃcient and robust computational techniques for identifying un- Linear neighborhood similarity discovered interactions. In this study, we present a label propagation model with linear neighborhood similarity, Label propagation 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 dis- eases lack known associations. Our results demonstrate that the LPLNS model based on the known disease-
Journal of Biomedical Informatics – Elsevier
Published: Jun 1, 2018
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