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A Gaussian Kernel Similarity-Based Linear Optimization Model for Predicting miRNA-lncRNA Interactions

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Intelligent Computing Theories and Application (ICIC 2020)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 12464))

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Abstract

MicroRNAs (miRNAs) and long non-coding RNAs (lncRNAs) are two main functional regulation non-coding RNAs, which involves many important pathological and physiological procedures. Accumulating evidences demonstrated that the interactions between miRNAs and lncRNAs have great impact on modulations of gene expression that are related to many Human diseases. However, identification of miRNA-lncRNA interactions via bio-experimental methods suffers from high cost and time consuming. Thus, it is more and more popular for researchers to utilize computational methods in miRNA-lncRNA interactions prediction because of their high-performance. In this study, we propose a gaussian kernel similarity-based linear optimization model for predicting miRNA-lncRNA interactions. Specifically, gaussian kernel similarity method is employed to learn the miRNAs and lncRNAs similarities based on the observed heterogeneous network. Then, an integrated network is constructed by combining the observed heterogeneous network and the constructed similarities. Finally, a linear optimization model is trained to obtain the rating matrix for the unobserved links in the integrated network. To evaluate the performance of our proposed method, k-fold cross-validation (CV) and leave-one-out cross-validation (LOOCV) are implemented on the collected dataset. The experimental results show that the proposed model yields high AUCs of 0.8624, 0.9053, 0.9152 and 0.9236 in 2-fold, 5-fold, 10-fold CV and LOOCV, respectively. It is anticipated that our proposed method is promising and reliable to inferring the interactions between miRNAs and lncRNAs for further biological researches.

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Acknowledgements

This work is supported by the NSFC Excellent Young Scholars Program, under Grants 61722212, in part by the National Science Foundation of China under Grants 61873212.

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Authors and Affiliations

  1. The Xinjiang Technical Institute of Physics and Chemistry, Chinese Academy of Sciences, Urumqi, 830011, China

    Leon Wong, Zhu-Hong You, Yu-An Huang & Xi Zhou

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

    Leon Wong

  3. Guang Dong Polytechnic College, Zhaoqing, 526100, Guangdong, China

    Mei-Yuan Cao

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  1. Leon Wong
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  2. Zhu-Hong You
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  4. Xi Zhou
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  5. Mei-Yuan Cao
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Contributions

L.W. conceived the project, developed the prediction method, designed the experiments, analyzed the result and wrote the manuscript. Z.H.Y. and Y.A.H analyzed the result and revised the manuscript. X.Z and M.Y.C. analyzed the result. All authors read and approved the manuscript.

Corresponding author

Correspondence to Zhu-Hong You .

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Editors and Affiliations

  1. Institute of Machine Learning and Systems Biology, Tongji University, Shanghai, China

    De-Shuang Huang

  2. School of Electrical Engineering, University of Ulsan, Ulsan, Korea (Republic of)

    Kang-Hyun Jo

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Wong, L., You, ZH., Huang, YA., Zhou, X., Cao, MY. (2020). A Gaussian Kernel Similarity-Based Linear Optimization Model for Predicting miRNA-lncRNA Interactions. In: Huang, DS., Jo, KH. (eds) Intelligent Computing Theories and Application. ICIC 2020. Lecture Notes in Computer Science(), vol 12464. Springer, Cham. https://doi.org/10.1007/978-3-030-60802-6_28

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  • DOI: https://doi.org/10.1007/978-3-030-60802-6_28

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