CircRNA-Disease Association Prediction Using Detailed Negative Set on Distance Metric-Based(DiNeg-CDA) 


Vol. 48,  No. 10, pp. 1289-1303, Oct.  2023
10.7840/kics.2023.48.10.1289


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  Abstract

ScircRNAs have been implicated in Alzheimer's and cardiovascular diseases, which are fatal diseases in humans. A lot of time and money are spent trying to identify circRNAs associated with dangerous diseases through biological experiments. One of the efficient approaches to save time or resources is to utilize deep learning. This study proposes a more sophisticated negative set construction method than the random negative set by deleting data similar to the positive with two criteria for the negative set existing in the vector space using distance metric. Through comparison experiments, the sophisticated Negative set performed better than the Random set. The model performance of DiNeg-CDA of the proposed model was measured with an AUC-ROC curve of 0.89.

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  Cite this article

[IEEE Style]

I. Hwang, S. Yoon, Jaein-Kim, K. Lee, "CircRNA-Disease Association Prediction Using Detailed Negative Set on Distance Metric-Based(DiNeg-CDA)," The Journal of Korean Institute of Communications and Information Sciences, vol. 48, no. 10, pp. 1289-1303, 2023. DOI: 10.7840/kics.2023.48.10.1289.

[ACM Style]

In-Woo Hwang, Seung-Won Yoon, Jaein-Kim, and Kyu-Chul Lee. 2023. CircRNA-Disease Association Prediction Using Detailed Negative Set on Distance Metric-Based(DiNeg-CDA). The Journal of Korean Institute of Communications and Information Sciences, 48, 10, (2023), 1289-1303. DOI: 10.7840/kics.2023.48.10.1289.

[KICS Style]

In-Woo Hwang, Seung-Won Yoon, Jaein-Kim, Kyu-Chul Lee, "CircRNA-Disease Association Prediction Using Detailed Negative Set on Distance Metric-Based(DiNeg-CDA)," The Journal of Korean Institute of Communications and Information Sciences, vol. 48, no. 10, pp. 1289-1303, 10. 2023. (https://doi.org/10.7840/kics.2023.48.10.1289)
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pISSN : 1226 - 4717
eISSN : 2287 - 3880