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Weighted Nonnegative Matrix Factorization Based on Multi-source Fusion Information for Predicting CircRNA-Disease Associations

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Intelligent Computing Theories and Application (ICIC 2021)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 12838))

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Abstract

Evidences increasingly have shown that circular RNAs (circRNAs) involve in various key biological processes. Because of the dysregulation and mutation of circRNAs are close associated with many complex human diseases, inferring the associations of circRNA with disease becomes an important step for understanding the pathogenesis, treatment and diagnosis of complex diseases. However, it is costly and time-consuming to verify the circRN-disease association through biological experiments, more and more computational methods have been proposed for inferring potential associations of circRNAs with diseases. In this work, we developed a novel weighted nonnegative matrix factorization algorithm based on multi-source fusion information for circRNA-disease association prediction (WNMFCDA). We firstly constructed the overall similarity of diseases based on semantic information and Gaussian Interaction Profile (GIP) kernel, and calculated the similarity of circRNAs based on GIP kernel. Next, the circRNA-disease adjacency matrix is rebuilt using K nearest neighbor profiles. Finally, nonnegative matrix factorization algorithm is utilized to calculate the scores of each pairs of circRNA and disease. To evaluate the performance of WNMFCDA, five-fold cross-validation is performed. WNMFCDA achieved the AUC value of 0.945, which is higher than other compared methods. In addition, we compared the prediction matrix with original adjacency matrix. These experimental results show that WNMFCDA is an effective algorithm for circRNA-disease association prediction.

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Acknowledgements

This work was supported in part by the NSFC Excellent Young Scholars Program, under Grant 61722212, in part by the National Natural Science Foundation of China, under Grant 62002297, in part by the Science and Technology Project of Jiangxi Provincial Department of Education, under Grants GJJ190834, GJJ201605.

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Authors and Affiliations

  1. School of Mathematics and Computer Science, Yichun University, Yichun, Jiangxi 336000, China

    Meineng Wang & Xuejun Xie

  2. School of Computer Science, Northwestern Polytechnical University, Xi’an, 710072, China

    Zhuhong You

  3. Xinjiang Technical Institutes of Physics and Chemistry, Chinese Academy of Sciences, Urumqi, 830011, China

    Leon Wong, Liping Li & Zhanheng Chen

  4. University of Chinese Academy of Sciences, Beijing, 100049, China

    Leon Wong & Zhanheng Chen

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  1. Meineng Wang
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  2. Xuejun Xie
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  3. Zhuhong You
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  4. Leon Wong
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  5. Liping Li
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  6. Zhanheng Chen
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Corresponding author

Correspondence to Zhuhong You .

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Editors and Affiliations

  1. Tongji University, Shanghai, China

    De-Shuang Huang

  2. University of Ulsan, Ulsan, Korea (Republic of)

    Kang-Hyun Jo

  3. Shenzhen University, Shenzhen, China

    Jianqiang Li

  4. Far Eastern Branch of the Russian Academy of Sciences, Vladivostok, Russia

    Valeriya Gribova

  5. University of Wollongong, North Wollongong, NSW, Australia

    Prashan Premaratne

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Wang, M., Xie, X., You, Z., Wong, L., Li, L., Chen, Z. (2021). Weighted Nonnegative Matrix Factorization Based on Multi-source Fusion Information for Predicting CircRNA-Disease Associations. In: Huang, DS., Jo, KH., Li, J., Gribova, V., Premaratne, P. (eds) Intelligent Computing Theories and Application. ICIC 2021. Lecture Notes in Computer Science(), vol 12838. Springer, Cham. https://doi.org/10.1007/978-3-030-84532-2_42

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  • DOI: https://doi.org/10.1007/978-3-030-84532-2_42

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