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FMLNCSIM：基于模糊测度的长链非编码RNA功能相似性计算模型。

FMLNCSIM: fuzzy measure-based lncRNA functional similarity calculation model.

作者信息

Chen Xing, Huang Yu-An, Wang Xue-Song, You Zhu-Hong, Chan Keith C C

机构信息

School of Information and Electrical Engineering, China University of Mining and Technology, Xuzhou, China.

Department of Computing, Hong Kong Polytechnic University, Hong Kong.

出版信息

Oncotarget. 2016 Jul 19;7(29):45948-45958. doi: 10.18632/oncotarget.10008.

DOI:10.18632/oncotarget.10008
PMID:27322210
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC5216773/
Abstract

Accumulating experimental studies have indicated the influence of lncRNAs on various critical biological processes as well as disease development and progression. Calculating lncRNA functional similarity is of high value in inferring lncRNA functions and identifying potential lncRNA-disease associations. However, little effort has been attempt to measure the functional similarity among lncRNAs on a large scale. In this study, we developed a Fuzzy Measure-based LNCRNA functional SIMilarity calculation model (FMLNCSIM) based on the assumption that functionally similar lncRNAs tend to be associated with similar diseases. The performance improvement of FMLNCSIM mainly comes from the combination of information content and the concept of fuzzy measure, which was applied to the directed acyclic graphs of disease MeSH descriptors. To evaluate the effectiveness of FMLNCSIM, we further combined it with the previously proposed model of Laplacian Regularized Least Squares for lncRNA-Disease Association (LRLSLDA). As a result, the integrated model, LRLSLDA-FMLNCSIM, achieve good performance in the frameworks of global LOOCV (AUCs of 0.8266 and 0.9338 based on LncRNADisease and MNDR database) and 5-fold cross validation (average AUCs of 0.7979 and 0.9237 based on LncRNADisease and MNDR database), which significantly improve the performance of previous classical models. It is anticipated that FMLNCSIM could be used for searching functionally similar lncRNAs and inferring lncRNA functions in the future researches.

摘要

越来越多的实验研究表明长链非编码RNA（lncRNAs）对各种关键生物学过程以及疾病的发生和发展具有影响。计算lncRNA功能相似性对于推断lncRNA功能和识别潜在的lncRNA-疾病关联具有很高的价值。然而，目前尚未有人大规模地尝试测量lncRNAs之间的功能相似性。在本研究中，我们基于功能相似的lncRNAs往往与相似疾病相关的假设，开发了一种基于模糊测度的lncRNA功能相似性计算模型（FMLNCSIM）。FMLNCSIM性能的提升主要源于信息内容与模糊测度概念的结合，该概念应用于疾病医学主题词表（MeSH）描述符的有向无环图。为了评估FMLNCSIM的有效性，我们进一步将其与先前提出的用于lncRNA-疾病关联的拉普拉斯正则化最小二乘模型（LRLSLDA）相结合。结果，整合模型LRLSLDA-FMLNCSIM在全局留一法交叉验证框架（基于LncRNADisease和MNDR数据库的AUC分别为0.8266和0.9338）和五折交叉验证（基于LncRNADisease和MNDR数据库的平均AUC分别为0.7979和0.9237）中表现良好，显著提高了先前经典模型的性能。预计FMLNCSIM可用于在未来研究中搜索功能相似的lncRNAs并推断lncRNA功能。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/bfff/5216773/2522525efcae/oncotarget-07-45948-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/bfff/5216773/f69c69adce6c/oncotarget-07-45948-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/bfff/5216773/85fc162cf514/oncotarget-07-45948-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/bfff/5216773/2522525efcae/oncotarget-07-45948-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/bfff/5216773/f69c69adce6c/oncotarget-07-45948-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/bfff/5216773/85fc162cf514/oncotarget-07-45948-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/bfff/5216773/2522525efcae/oncotarget-07-45948-g003.jpg

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Sci Rep. 2016 Feb 16;6:21106. doi: 10.1038/srep21106.
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miREFRWR: a novel disease-related microRNA-environmental factor interactions prediction method.miREFRWR：一种新型的疾病相关微小RNA-环境因子相互作用预测方法。
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BMC Bioinformatics. 2022 Feb 25;22(Suppl 12):621. doi: 10.1186/s12859-022-04579-0.
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Fusion of KATZ measure and space projection to fast probe potential lncRNA-disease associations in bipartite graphs.KATZ 测度与空间投影融合方法快速探测二分图中长链非编码 RNA 与疾病的关联。
PLoS One. 2021 Nov 22;16(11):e0260329. doi: 10.1371/journal.pone.0260329. eCollection 2021.
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