共 38 条
Prediction of LncRNA-Disease Associations Based on Network Consistency Projection
被引:45
作者:
Li, Guanghui
[1
]
Luo, Jiawei
[2
]
Liang, Cheng
[3
]
Xiao, Qiu
[4
]
Ding, Pingjian
[2
]
Zhang, Yuejin
[1
]
机构:
[1] East China Jiaotong Univ, Sch Informat Engn, Nanchang 330013, Jiangxi, Peoples R China
[2] Hunan Univ, Coll Comp Sci & Elect Engn, Changsha 410082, Hunan, Peoples R China
[3] Shandong Normal Univ, Coll Informat Sci & Engn, Jinan 250000, Shandong, Peoples R China
[4] Hunan Normal Univ, Coll Informat Sci & Engn, Changsha 410081, Hunan, Peoples R China
来源:
基金:
中国国家自然科学基金;
关键词:
Disease-related lncRNAs;
lncRNA-disease association;
network consistency projection;
similarity measure;
LONG NONCODING RNAS;
FUNCTIONAL SIMILARITY;
DATABASE;
CELLS;
D O I:
10.1109/ACCESS.2019.2914533
中图分类号:
TP [自动化技术、计算机技术];
学科分类号:
0812 ;
摘要:
A growing body of research has uncovered the role of long noncoding RNAs (lncRNAs) in multiple biological processes and tumorigenesis. Predicting novel interactions between diseases and lncRNAs could help decipher disease pathology and discover new drugs. However, because of a lack of data, inferring disease-lncRNA associations accurately and efficiently remains a challenge. In this paper, we present a novel network consistency projection for LncRNA-disease association prediction (NCPLDA) model by integrating the lncRNA-disease association probability matrix with the integrated disease similarity and lncRNA similarity. The lncRNA-disease association probability matrix is calculated based on known lncRNA-disease associations and disease semantic similarity. The integrated disease similarity and lncRNA similarity are computed based on disease semantic similarity, lncRNA functional similarity and Gaussian interaction profile kernel similarity. In leave-one-out cross validation experiments, NCPLDA achieved outstanding AUCs of 0.8900, 0.8996, and 0.9012 for three datasets. Furthermore, prostate cancer and ovarian cancer case studies demonstrated that the NCPLDA can effectively infer undiscovered lncRNAs.
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页码:58849 / 58856
页数:8
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