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A decision support system based on multi-sources information to predict piRNA–disease associations using stacked autoencoder

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Abstract

Despite experimental efforts to map out the human interactome, the continued high data complexity, heterogeneity, and combined explosion limit our ability to understand the molecular roots of human disease. Computational tools provide a promising alternative when identifying biologically significant, yet unmapped associations between PIWI-interacting RNA (piRNA) and diseases. However, few predictors for large-scale prediction of potential disease-associated piRNAs have been proposed to provide effective candidates for future biomedical research now. In this study, we proposed a new computational model based on multi-source information and stacked autoencoder, called MSRDA, to predict potential piRNA–disease associations on a large scale. In particular, we introduce stacked autoencoders into the field of piRNA–disease association prediction for the first time. In the fivefold cross-validation, the MSRDA achieves the average area under the curve of 0.9184 ± 0.0015. To further evaluate the performance of MSRDA, we compared models without feature denoising and models with different types of feature representations. Moreover, the proposed method is optimal compared to related works. In conclusion, MSRDA is an effective tool that provides new impetus for uncovering the root causes of human disease.

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Acknowledgements

This work is supported by the National Natural Science Foundation of China (No. 62072473, No.61772552, No.61972423, and No.61832019), the Xinjiang Natural Science Foundation under Grant 2017D01A78, and Fundamental Research Funds for the Central Universities of Central South University (2021zzts0206).

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Authors and Affiliations

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

    Kai Zheng

  2. College of Arts and Sciences, The Ohio State University, Columbus, USA

    Ying Liang

  3. College of Mathematics and Systems Science, Shandong University of Science and Technology, Qingdao, China

    Yue-Ying Liu

  4. College of Oceanography and Space Informatics, China University of Petroleum, Qingdao, China

    Muhammad Yasir

  5. College of Geodesy and Geomatics, Shandong University of Science and Technology, Qingdao, China

    Ping Wang

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  1. Kai Zheng
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Correspondence to Ping Wang.

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Zheng, K., Liang, Y., Liu, YY. et al. A decision support system based on multi-sources information to predict piRNA–disease associations using stacked autoencoder. Soft Comput 26, 11007–11016 (2022). https://doi.org/10.1007/s00500-022-07396-y

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