首页|Identification of human microRNA-disease association via low-rank approximation-based link propagation and multiple kernel learning

Identification of human microRNA-disease association via low-rank approximation-based link propagation and multiple kernel learning

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Numerous studies have demonstrated that human microRNAs(miRNAs)and diseases are associated and studies on the microRNA-disease association(MDA)have been conducted.We developed a model using a low-rank approximation-based link propagation algorithm with Hilbert-Schmidt independence criterion-based multiple kernel learning(HSIC-MKL)to solve the problem of the large time commitment and cost of traditional biological experiments involving miRNAs and diseases,and improve the model effect.We constructed three kernels in miRNA and disease space and conducted kernel fusion using HSIC-MKL.Link propagation uses matrix factorization and matrix approximation to effectively reduce computation and time costs.The results of the experiment show that the approach we proposed has a good effect,and,in some respects,exceeds what existing models can do.

human miRNA-disease associationmultiple kernel learninglink propagationmiRNA similaritydisease similarity

Yizheng WANG、Xin ZHANG、Ying JU、Qing LIU、Quan ZOU、Yazhou ZHANG、Yijie DING、Ying ZHANG

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Institute of Fundamental and Frontier Sciences,University of Electronic Science and Technology of China,Chengdu 610054,China

Yangtze Delta Region Institute(Quzhou),University of Electronic Science and Technology of China,Quzhou 324000,China

Beidahuang Industry Group General Hospital,Harbin 150088,China

School of Informatics,Xiamen University,Xiamen 361005,China

Department of Anesthesiology,Hospital(T.C.M)Affiliated to Southwest Medical University,Luzhou 646000,China

Software Engineering College,Zhengzhou University of Light Industry,Zhengzhou 450002,China

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国家自然科学基金国家自然科学基金国家自然科学基金Municipal Government of Quzhou浙江省自然科学基金

6207238562172076U22A20382022D040LY23F020003

2024

10.1007/s11704-023-2490-5

计算机科学前沿
高等教育出版社

计算机科学前沿

CSTPCDEI
影响因子：0.303
ISSN：2095-2228
年,卷(期)：2024.18(2)
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