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Identification of Virus-Receptor Interactions Based on Network Enhancement and Similarity

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Bioinformatics Research and Applications (ISBRA 2020)

Part of the book series: Lecture Notes in Computer Science ((LNBI,volume 12304))

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Abstract

As a main composition of the human-associated microbiome, viruses are directly associated with our health and disease. The receptor-binding is critical for the virus infection. So identifying potential virus-receptor interactions will help systematically understand the mechanisms of virus-receptor interactions and effectively treat infectious diseases caused by viruses. Several computational models have been developed to identify virus-receptor interactions based on assumption that similar viruses show similar interaction patterns with receptors and vice versa, but the performance need to be improved. Furthermore, the virus network and the receptor network are also noisy. Therefore, we present a new prediction model (NERLS) to identify potential virus-receptor interactions based on Network Enhancement, virus sequence information and receptor sequence information by Regularized Least Squares. Firstly, the virus network is constructed based on the virus sequence similarity and Gaussian interaction profile (GIP) kernel similarity of viruses by a mean method. They are calculated based on the viral RefSeq genomes downloaded from NCBI and known virus-receptor interactions, respectively. Similarly, we also use the same mean method to construct the receptor network based on the amino acid sequence similarity and known virus-receptor interactions. Then Network Enhancement is applied to denoise the virus network and the receptor network. Finally, we employ the regularized least squares algorithm to identify potential virus-receptor interactions. The 10-fold cross validation (10CV) experimental results indicate that an average Area Under Curve (AUC) values of NERLS is 0.8930, which is superior to other computing models of 0.8675 (IILLS), 0.7959 (BRWH), 0.7577 (LapRLS), and 0.7128 (CMF). Furthermore, the Leave One Out Cross Validation (LOOCV) experimental results also show that NERLS can achieve the AUC values of 0.9210, which is better than other models (IILLS: 0.9061, BRWH: 0.8105, LapRLS: 0.7713, CMF: 0.7491). In addition, a case study also confirms the effectiveness of NERLS in predicting potential virus-receptor interactions.

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Acknowledgement

This work is supported in part by the National Natural Science Foundation of China (No. 61772552, No. 61420106009, No. 61832019 and No. 61962050), 111 Project (No. B18059), Hunan Provincial Science and Technology Program (No. 2018WK4001), the Science and Technology Foundation of Guizhou Province of China under Grant NO. [2020]1Y264, the Scientific Research Foundation of Hunan Provincial Education Department (No. 18B469), and the Aid Program Science and Technology Innovative Research Team of Hunan Institute of Technology.

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Authors and Affiliations

  1. Hunan Provincial Key Lab on Bioinformatics, School of Computer Science and Engineering, Central South University, Changsha, 410083, China

    Lingzhi Zhu, Cheng Yan & Guihua Duan

  2. School of Computer and Information Science, Hunan Institute of Technology, Hengyang, 421008, China

    Lingzhi Zhu

  3. School of Computer and Information, Qiannan Normal University for Nationalities, Duyun, 558000, China

    Cheng Yan

Authors
  1. Lingzhi Zhu
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  2. Cheng Yan
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  3. Guihua Duan
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Corresponding author

Correspondence to Cheng Yan .

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Editors and Affiliations

  1. Department of Computer Science, Georgia State University, Atlanta, GA, USA

    Zhipeng Cai

  2. University of Connecticut, Storrs Mansfield, CT, USA

    Ion Mandoiu

  3. Florida International University, Miami, FL, USA

    Giri Narasimhan

  4. Georgia State University, Atlanta, GA, USA

    Pavel Skums

  5. University of North Texas, Denton, TX, USA

    Xuan Guo

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Zhu, L., Yan, C., Duan, G. (2020). Identification of Virus-Receptor Interactions Based on Network Enhancement and Similarity. In: Cai, Z., Mandoiu, I., Narasimhan, G., Skums, P., Guo, X. (eds) Bioinformatics Research and Applications. ISBRA 2020. Lecture Notes in Computer Science(), vol 12304. Springer, Cham. https://doi.org/10.1007/978-3-030-57821-3_33

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  • DOI: https://doi.org/10.1007/978-3-030-57821-3_33

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-57820-6

  • Online ISBN: 978-3-030-57821-3

  • eBook Packages: Computer ScienceComputer Science (R0)

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