IMC-MDA: Prediction of miRNA-disease association based on induction matrix completion

Zejun Li; Yuxiang Zhang; Yuting Bai; Xiaohui Xie; Lijun Zeng; Zejun Li; Yuxiang Zhang; Yuting Bai; Xiaohui Xie; Lijun Zeng

doi:10.3934/mbe.2023471

Mathematical Biosciences and Engineering

2023, Volume 20, Issue 6: 10659-10674. doi: 10.3934/mbe.2023471

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Research article

IMC-MDA: Prediction of miRNA-disease association based on induction matrix completion

1.
School of Computer and Information Science, Hunan Institute of Technology, Hengyang 412002, China
2.
School of Computer and Artificial Intelligence, Zhengzhou University, Zhengzhou, Henan, 450001, China
3.
College of Information Science and Engineering, Hunan University, Changsha 410082, Hunan, China

Received: 05 October 2022 Revised: 15 March 2023 Accepted: 16 March 2023 Published: 13 April 2023

To comprehend the etiology and pathogenesis of many illnesses, it is essential to identify disease-associated microRNAs (miRNAs). However, there are a number of challenges with current computational approaches, such as the lack of "negative samples", that is, confirmed irrelevant miRNA-disease pairs, and the poor performance in terms of predicting miRNAs related with "isolated diseases", i.e. illnesses with no known associated miRNAs, which presents the need for novel computational methods. In this study, for the purpose of predicting the connection between disease and miRNA, an inductive matrix completion model was designed, referred to as IMC-MDA. In the model of IMC-MDA, for each miRNA-disease pair, the predicted marks are calculated by combining the known miRNA-disease connection with the integrated disease similarities and miRNA similarities. Based on LOOCV, IMC-MDA had an AUC of 0.8034, which shows better performance than previous methods. Furthermore, experiments have validated the prediction of disease-related miRNAs for three major human diseases: colon cancer, kidney cancer, and lung cancer.
- miRNA-disease,
- miRNAs,
- disease,
- matrix completion,
- directed acyclic graphs
Citation: Zejun Li, Yuxiang Zhang, Yuting Bai, Xiaohui Xie, Lijun Zeng. IMC-MDA: Prediction of miRNA-disease association based on induction matrix completion[J]. Mathematical Biosciences and Engineering, 2023, 20(6): 10659-10674. doi: 10.3934/mbe.2023471

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Abstract

To comprehend the etiology and pathogenesis of many illnesses, it is essential to identify disease-associated microRNAs (miRNAs). However, there are a number of challenges with current computational approaches, such as the lack of "negative samples", that is, confirmed irrelevant miRNA-disease pairs, and the poor performance in terms of predicting miRNAs related with "isolated diseases", i.e. illnesses with no known associated miRNAs, which presents the need for novel computational methods. In this study, for the purpose of predicting the connection between disease and miRNA, an inductive matrix completion model was designed, referred to as IMC-MDA. In the model of IMC-MDA, for each miRNA-disease pair, the predicted marks are calculated by combining the known miRNA-disease connection with the integrated disease similarities and miRNA similarities. Based on LOOCV, IMC-MDA had an AUC of 0.8034, which shows better performance than previous methods. Furthermore, experiments have validated the prediction of disease-related miRNAs for three major human diseases: colon cancer, kidney cancer, and lung cancer.

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