MDAlmc: A Novel Low-rank Matrix Completion Model for MiRNADisease Association Prediction by Integrating Similarities among MiRNAs and Diseases.

IF 3.8 4区 医学 Q2 GENETICS & HEREDITY
Kun Wang, Junlin Xu, Geng Tian, Yang Li, Xueying Zeng, Jialiang Yang
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引用次数: 0

Abstract

Introduction: The importance of microRNAs (miRNAs) has been emphasized by an increasing number of studies, and it is well-known that miRNA dysregulation is associated with a variety of complex diseases. Revealing the associations between miRNAs and diseases are essential to disease prevention, diagnosis, and treatment.

Methods: However, traditional experimental methods in validating the roles of miRNAs in diseases could be very expensive, labor-intensive and time-consuming. Thus, there is a growing interest in predicting miRNA-disease associations by computational methods. Though many computational methods are in this category, their prediction accuracy needs further improvement for downstream experimental validation. In this study, we proposed a novel model to predict miRNA-disease associations by low-rank matrix completion (MDAlmc) integrating miRNA functional similarity, disease semantic similarity, and known miRNA-disease associations. In the 5-fold cross-validation, MDAlmc achieved an average AUROC of 0.8709 and AUPRC of 0.4172, better than those of previous models.

Results: Among the case studies of three important human diseases, the top 50 predicted miRNAs of 96% (breast tumors), 98% (lung tumors), and 90% (ovarian tumors) have been confirmed by previous literatures. And the unconfirmed miRNAs were also validated to be potential disease-associated miRNAs.

Conclusion: MDAlmc is a valuable computational resource for miRNA-disease association prediction.

MDAlmc:一种新的低秩矩阵补全模型,通过整合mirna和疾病之间的相似性来预测mirna疾病关联。
导读:越来越多的研究强调了microRNAs (miRNAs)的重要性,众所周知,miRNA失调与多种复杂疾病有关。揭示mirna与疾病之间的关联对于疾病的预防、诊断和治疗至关重要。方法:然而,验证mirna在疾病中的作用的传统实验方法可能非常昂贵,劳动密集型和耗时。因此,通过计算方法预测mirna -疾病关联的兴趣越来越大。虽然这类计算方法很多,但其预测精度有待进一步提高,有待下游实验验证。在这项研究中,我们提出了一种新的模型,通过低秩矩阵完成(MDAlmc)整合miRNA功能相似性、疾病语义相似性和已知的miRNA-疾病相关性来预测miRNA-疾病关联。在5重交叉验证中,MDAlmc的平均AUROC为0.8709,AUPRC为0.4172,优于以往的模型。结果:在人类三种重要疾病的病例研究中,96%(乳腺肿瘤)、98%(肺肿瘤)和90%(卵巢肿瘤)的前50个预测mirna已被既往文献证实。未经证实的mirna也被证实是潜在的疾病相关mirna。结论:MDAlmc是预测mirna与疾病关联的宝贵计算资源。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Current gene therapy
Current gene therapy 医学-遗传学
CiteScore
6.70
自引率
2.80%
发文量
46
期刊介绍: Current Gene Therapy is a bi-monthly peer-reviewed journal aimed at academic and industrial scientists with an interest in major topics concerning basic research and clinical applications of gene and cell therapy of diseases. Cell therapy manuscripts can also include application in diseases when cells have been genetically modified. Current Gene Therapy publishes full-length/mini reviews and original research on the latest developments in gene transfer and gene expression analysis, vector development, cellular genetic engineering, animal models and human clinical applications of gene and cell therapy for the treatment of diseases. Current Gene Therapy publishes reviews and original research containing experimental data on gene and cell therapy. The journal also includes manuscripts on technological advances, ethical and regulatory considerations of gene and cell therapy. Reviews should provide the reader with a comprehensive assessment of any area of experimental biology applied to molecular medicine that is not only of significance within a particular field of gene therapy and cell therapy but also of interest to investigators in other fields. Authors are encouraged to provide their own assessment and vision for future advances. Reviews are also welcome on late breaking discoveries on which substantial literature has not yet been amassed. Such reviews provide a forum for sharply focused topics of recent experimental investigations in gene therapy primarily to make these results accessible to both clinical and basic researchers. Manuscripts containing experimental data should be original data, not previously published.
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