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Predicting Human Disease-Associated piRNAs Based on Multi-source Information and Random Forest

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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

Whole genome analysis studies have shown that Piwi-interacting RNA (piRNA) play a crucial role in disease progression, diagnosis, and therapeutic target. However, traditional biological experiments are expensive and time-consuming. Thus, computational models could serve as a complementary means to provide potential disease-related piRNA candidates. In this study, we propose a novel computational model called APDA to identify piRNA-disease associations. The proposed method integrates disease semantic similarity and piRNA sequence information to construct feature vectors, and maps them to the optimal feature subspace through the stacked autoencoder to obtain the final feature vector. Finally, random forest classifier is used to infer disease-related piRNA. In five-fold cross-validation, the APDA achieved an average AUC of 0.9088 and standard deviation of 0.0126, which is significantly better than the compared method. Therefore, the proposed APDA method is a powerful and necessary tool for predicting human disease-associated piRNAs and provide new impetus to reveal the underlying causes of human disease.

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Acknowledgments

This work is supported by the Xinjiang Natural Science Foundation under Grant 2017D01A78.

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Authors and Affiliations

  1. School of Computer Science and Engineering, Central South University, Changsha, China

    Kai Zheng

  2. Xinjiang Technical Institute of Physics and Chemistry, Chinese Academy of Sciences, Urumqi, China

    Zhu-Hong You, Lei Wang, Hao-Yuan Li & Bo-Ya Ji

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  1. Kai Zheng
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  2. Zhu-Hong You
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  3. Lei Wang
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  4. Hao-Yuan Li
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  5. Bo-Ya Ji
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Correspondence to Zhu-Hong You or Lei Wang .

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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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Zheng, K., You, ZH., Wang, L., Li, HY., Ji, BY. (2020). Predicting Human Disease-Associated piRNAs Based on Multi-source Information and Random Forest. 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_20

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  • DOI: https://doi.org/10.1007/978-3-030-60802-6_20

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