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LOMDA: Linear optimization for miRNA-disease association prediction

View ORCID ProfileRatha Pech, Yan-Li Lee, Dong Hao, Maryna Po, Tao Zhou
doi: https://doi.org/10.1101/751651
Ratha Pech
CompleX Lab, University of Electronic Science and Technology of China, Chengdu 611731, People’s Republic of ChinaBig Data Research Center, University of Electronic Science and Technology of China, Chengdu 611731, People’s Republic of China
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  • ORCID record for Ratha Pech
Yan-Li Lee
CompleX Lab, University of Electronic Science and Technology of China, Chengdu 611731, People’s Republic of ChinaBig Data Research Center, University of Electronic Science and Technology of China, Chengdu 611731, People’s Republic of China
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Dong Hao
CompleX Lab, University of Electronic Science and Technology of China, Chengdu 611731, People’s Republic of ChinaBig Data Research Center, University of Electronic Science and Technology of China, Chengdu 611731, People’s Republic of China
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Maryna Po
Department of Chemistry and Biochemistry, George Mason University, Virginia 22030, USA
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Tao Zhou
CompleX Lab, University of Electronic Science and Technology of China, Chengdu 611731, People’s Republic of ChinaBig Data Research Center, University of Electronic Science and Technology of China, Chengdu 611731, People’s Republic of China
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  • For correspondence: zhutou@ustc.edu
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Abstract

MicroRNAs (miRNAs) have been playing a crucial role in many important biological processes. Currently, the validated associations between miRNAs and diseases are insufficient comparing to all underlying associations. To identify these hidden associations based on biological experiment is expensive, laborious and time consuming. Therefore, computationally inferring the potential associations from biological data for further biological experiment has attracted increasing interests from different communities ranging from biological to computational science. In this work, we propose an effective and flexible method to predict the associations between miRNAs and diseases, namely linear optimization (LOMDA). The proposed method is capable of predicting the associations in three manners e.g., extra information such as miRNA functional similarity, gene functional similarity and known miRNA-disease associations are available; only some associations are known; and new miRNAs or diseases that do not have any known associations at all. The average AUC obtained from LOMDA over 15 diseases in a 5-fold-cross validation is 0.997, while the AUC of 5-fold cross validation on all diseases is 0.957. Moreover, the average AUC on leave-one-out cross validation is 0.866. We compare LOMDA with the state-of-the-art methods and the results show that LOMDA outperforms the others in both cases, e.g., extra information is combined and only known associations are used.

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  • https://github.com/rathapech/LOMDA

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The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-NC-ND 4.0 International license.
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Posted August 31, 2019.
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LOMDA: Linear optimization for miRNA-disease association prediction
Ratha Pech, Yan-Li Lee, Dong Hao, Maryna Po, Tao Zhou
bioRxiv 751651; doi: https://doi.org/10.1101/751651
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LOMDA: Linear optimization for miRNA-disease association prediction
Ratha Pech, Yan-Li Lee, Dong Hao, Maryna Po, Tao Zhou
bioRxiv 751651; doi: https://doi.org/10.1101/751651

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