Evidence map›Paper›PMID 32425979›Full record

ArticleFrontiers in genetics2020

BHCMDA: A New Biased Heat Conduction Based Method for Potential MiRNA-Disease Association Prediction.

Xianyou Zhu, Xuzai Wang, Haochen Zhao, Tingrui Pei, Linai Kuang, Lei Wang

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Article in Frontiers in genetics, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed
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1 · What the graph read from it

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4 citing papers in PubMed.

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5 · Who and what money

Authors and funding

6 authors.

Xianyou ZhuCollege of Computer Science and Technology, Hengyang Normal University, Hengyang, China.
Xuzai WangKey Laboratory of Hunan Province for Internet of Things and Information Security, Xiangtan University, Xiangtan, China.
Haochen ZhaoKey Laboratory of Hunan Province for Internet of Things and Information Security, Xiangtan University, Xiangtan, China.
Tingrui PeiKey Laboratory of Hunan Province for Internet of Things and Information Security, Xiangtan University, Xiangtan, China.
Linai KuangCollege of Computer Science and Technology, Hengyang Normal University, Hengyang, China.
Lei WangKey Laboratory of Hunan Province for Internet of Things and Information Security, Xiangtan University, Xiangtan, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Recent studies have indicated that microRNAs (miRNAs) are closely related to sundry human sophisticated diseases. According to the surmise that functionally similar miRNAs are more likely associated with phenotypically similar diseases, researchers have proposed a variety of valid computational models through integrating known miRNA-disease associations, disease semantic similarity, miRNA functional similarity, and Gaussian interaction profile kernel similarity to discover the potential miRNA-disease relationships in biomedical researches. Taking account of the limitations of previous computational models, a new computational model based on biased heat conduction for MiRNA-Disease Association prediction (BHCMDA) was proposed in this paper, which can achieve the AUC of 0.8890 in LOOCV (Leave-One-Out Cross Validation) and the mean AUC of 0.9060, 0.8931 under the framework of twofold cross validation, fivefold cross validation, respectively. In addition, BHCMDA was further implemented to the case studies of three vital human cancers, and simulation results illustrated that there were 88% (Esophageal Neoplasms), 92% (Colonic Neoplasms) and 92% (Lymphoma) out of top 50 predicted miRNAs having been confirmed by experimental literatures, separately, which demonstrated the good performance of BHCMDA as well. Thence, BHCMDA would be a useful calculative resource for potential miRNA-disease association prediction.

Indexed as

biased heat conductionbipartite graph networkclustering algorithmintegrated similaritymiRNA-disease association

Identifiers

PMID32425979
PMCPMC7212362

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