Li Xiaoying, Lin Yaping, Gu Changlong, Yang Jialiang
College of Computer Science and Electronic Engineering, Hunan University, Changsha, 410082, Hunan, China.
Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA.
BMC Syst Biol. 2019 Apr 5;13(Suppl 2):26. doi: 10.1186/s12918-019-0696-9.
Biological experiments have confirmed the association between miRNAs and various diseases. However, such experiments are costly and time consuming. Computational methods help select potential disease-related miRNAs to improve the efficiency of biological experiments.
In this work, we develop a novel method using multiple types of data to calculate miRNA and disease similarity based on mutual information, and add miRNA family and cluster information to predict human disease-related miRNAs (FCMDAP). This method not only depends on known miRNA-diseases associations but also accurately measures miRNA and disease similarity and resolves the problem of overestimation. FCMDAP uses the k most similar neighbor recommendation algorithm to predict the association score between miRNA and disease. Information about miRNA cluster is also used to improve prediction accuracy.
FCMDAP achieves an average AUC of 0.9165 based on leave-one-out cross validation. Results confirm the 100, 98 and 96% of the top 50 predicted miRNAs reported in case studies on colorectal, lung, and pancreatic neoplasms. FCMDAP also exhibits satisfactory performance in predicting diseases without any related miRNAs and miRNAs without any related diseases.
In this study, we present a computational method FCMDAP to improve the prediction accuracy of disease related miRNAs. FCMDAP could be an effective tool for further biological experiments.
生物学实验已证实微小RNA（miRNA）与多种疾病之间存在关联。然而，此类实验成本高昂且耗时。计算方法有助于筛选出潜在的疾病相关miRNA，以提高生物学实验的效率。
在本研究中，我们开发了一种新方法，利用多种类型的数据，基于互信息计算miRNA与疾病的相似度，并加入miRNA家族和聚类信息来预测人类疾病相关的miRNA（FCMDAP）。该方法不仅依赖于已知的miRNA-疾病关联，还能准确衡量miRNA与疾病的相似度，并解决了高估问题。FCMDAP使用k最近邻推荐算法来预测miRNA与疾病之间的关联分数。miRNA聚类信息也用于提高预测准确性。
基于留一法交叉验证，FCMDAP的平均曲线下面积（AUC）达到0.9165。结果证实了在结直肠癌、肺癌和胰腺癌病例研究中报告的前50个预测miRNA中的100%、98%和96%。FCMDAP在预测无任何相关miRNA的疾病和无任何相关疾病的miRNA方面也表现出令人满意的性能。
在本研究中，我们提出了一种计算方法FCMDAP，以提高疾病相关miRNA的预测准确性。FCMDAP可能是进一步生物学实验的有效工具。
