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GCNPCA: miRNA-Disease Associations Prediction Algorithm Based on Graph Convolutional Neural Networks

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成果类型:
期刊论文
作者:
Liu, Jiwen;Kuang, Zhufang;Deng, Lei
作者机构:
[Kuang, Zhufang; Liu, Jiwen] Cent South Univ Forestry & Technol, Sch Comp & Informat Engn, Changsha 410004, Hunan, Peoples R China.
[Deng, Lei] Cent South Univ, Sch Comp Sci & Engn, Changsha 410083, Peoples R China.
语种:
英文
关键词:
Diseases;Prediction algorithms;Neoplasms;Feature extraction;Semantics;Databases;Principal component analysis;miRNA-disease;graph convolutional network;heterogenous network;principal component analysis;random forest
期刊:
IEEE/ACM Transactions on Computational Biology and Bioinformatics
ISSN:
1545-5963
年:
2023
卷:
20
期:
2
页码:
1041-1052
基金类别:
10.13039/501100001809-National Natural Science Foundation of China (Grant Number: 62072477, 61309027, 61702562 and 61702561) 10.13039/501100004735-Natural Science Foundation of Hunan Province (Grant Number: 2018JJ3888) Scientific Research Fund of Hunan Provincial Education Department (Grant Number: 18B197) National Key R&D Program of China (Grant Number: 2018YFB1700200) Hunan Key Laboratory of Intelligent Logistics Technology (Grant Number: 2019TP1015)
机构署名:
本校为第一机构
院系归属:
计算机与信息工程学院
摘要:
A growing number of studies have confirmed the important role of microRNAs (miRNAs) in human diseases and the aberrant expression of miRNAs affects the onset and progression of human diseases. The discovery of disease-associated miRNAs as new biomarkers promote the progress of disease pathology and clinical medicine. However, only a small proportion of miRNA-disease correlations have been validated by biological experiments. And identifying miRNA-disease associations through biological experiments is both expensive and inefficient. Therefore, it is important to develop efficient and highly acc...

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