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Identification of miRNA-lncRNA Underlying Interactions Through Representation for Multiplex Heterogeneous Network

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Intelligent Computing Theories and Application (ICIC 2022)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 13394))

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Abstract

In several investigations of cancers, non-coding RNAs, especially lncRNAs (long non-coding RNAs) and miRNAs(microRNAs) have been proven that they are strongly relevant to diseases. For instance, neoplasms and non-small cell lung cancer are regulated by miRNA and lncRNA. However, it is complex that cancer could be co-regulated by multiple genes at the same time. Furthermore, different miRNAs and lncRNAs may also have interactions and regulations with others. The interactions among multiple genes still need to be interpreted. Traditional biology experiments are time-consumed and expensive. Increasing number of computational predictions of lncRNA-miRNA interactions have been seen as an alternative strategy to the biology methods for predict potential interactions. Considering that the complexity of molecular interactions, it should be more globally in identification underlying associations. We proposed a method using representation learning for attributed multiplex heterogeneous network. We conduct systematical evaluations for the model. The proposed method achieved ROC-AUC of 0.9180, PR-AUC of 0.8438, F1 scores of 0.7677. This method incorporated more biomolecular network information and provided more possibilities for discovering underlying bioinformatic associations.

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Authors and Affiliations

  1. Northwestern Polytechnical University, DongXiang Road, Xian, China

    Jiren Zhou, Zhuhong You, Xuequn Shang, Rui Niu & Yue Yun

Authors
  1. Jiren Zhou
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  2. Zhuhong You
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  3. Xuequn Shang
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  4. Rui Niu
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  5. Yue Yun
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Corresponding author

Correspondence to Zhuhong You .

Editor information

Editors and Affiliations

  1. Tongji University, Shanghai, China

    De-Shuang Huang

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

    Kang-Hyun Jo

  3. Xi’an Polytechnic University, Xi’an, China

    Junfeng Jing

  4. The University of Wollongong, North Wollongong, NSW, Australia

    Prashan Premaratne

  5. Polytecnic of Bari, Bari, Italy

    Vitoantonio Bevilacqua

  6. Liverpool John Moores University, Liverpool, UK

    Abir Hussain

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Zhou, J., You, Z., Shang, X., Niu, R., Yun, Y. (2022). Identification of miRNA-lncRNA Underlying Interactions Through Representation for Multiplex Heterogeneous Network. In: Huang, DS., Jo, KH., Jing, J., Premaratne, P., Bevilacqua, V., Hussain, A. (eds) Intelligent Computing Theories and Application. ICIC 2022. Lecture Notes in Computer Science, vol 13394. Springer, Cham. https://doi.org/10.1007/978-3-031-13829-4_22

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  • DOI: https://doi.org/10.1007/978-3-031-13829-4_22

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-13828-7

  • Online ISBN: 978-3-031-13829-4

  • eBook Packages: Computer ScienceComputer Science (R0)

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