疾病
新颖性
计算机科学
集合(抽象数据类型)
交叉验证
计算生物学
相关性
编码(社会科学)
生物信息学
生物
机器学习
医学
数学
统计
病理
心理学
几何学
社会心理学
程序设计语言
作者
Linai Kuang,Haochen Zhao,Lei Wang,Zhanwei Xuan,Tingrui Pei
出处
期刊:Current Bioinformatics
[Bentham Science]
日期:2019-04-10
卷期号:14 (4): 333-343
被引量:11
标识
DOI:10.2174/1574893613666181026122045
摘要
Background: In recent years, more evidence have progressively indicated that Long non-coding RNAs (lncRNAs) play vital roles in wide-ranging human diseases, which can serve as potential biomarkers and drug targets. Comparing with vast lncRNAs being found, the relationships between lncRNAs and diseases remain largely unknown. Objective: The prediction of novel and potential associations between lncRNAs and diseases would contribute to dissect the complex mechanisms of disease pathogenesis. associations while known disease-lncRNA associations are required only. Method: In this paper, a new computational method based on Point Cut Set is proposed to predict LncRNA-Disease Associations (PCSLDA) based on known lncRNA-disease associations. Compared with the existing state-of-the-art methods, the major novelty of PCSLDA lies in the incorporation of distance difference matrix and point cut set to set the distance correlation coefficient of nodes in the lncRNA-disease interaction network. Hence, PCSLDA can be applied to forecast potential lncRNAdisease associations while known disease-lncRNA associations are required only. Results: Simulation results show that PCSLDA can significantly outperform previous state-of-the-art methods with reliable AUC of 0.8902 in the leave-one-out cross-validation and AUCs of 0.7634 and 0.8317 in 5-fold cross-validation and 10-fold cross-validation respectively. And additionally, 70% of top 10 predicted cancer-lncRNA associations can be confirmed. Conclusion: It is anticipated that our proposed model can be a great addition to the biomedical research field.
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