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DISMIRA：基于最大加权匹配推理模型和基于基序的分析对miRNA-疾病关联中的疾病候选物进行优先级排序。

DISMIRA: Prioritization of disease candidates in miRNA-disease associations based on maximum weighted matching inference model and motif-based analysis.

作者信息

Nalluri Joseph J, Kamapantula Bhanu K, Barh Debmalya, Jain Neha, Bhattacharya Antaripa, Almeida Sintia Silva de, Ramos Rommel Thiago Juca, Silva Artur, Azevedo Vasco, Ghosh Preetam

出版信息

BMC Genomics. 2015;16 Suppl 5(Suppl 5):S12. doi: 10.1186/1471-2164-16-S5-S12. Epub 2015 May 26.

DOI:10.1186/1471-2164-16-S5-S12
PMID:26040329
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC4461020/
Abstract

BACKGROUND

MicroRNAs (miRNAs) have increasingly been found to regulate diseases at a significant level. The interaction of miRNA and diseases is a complex web of multilevel interactions, given the fact that a miRNA regulates upto 50 or more diseases and miRNAs/diseases work in clusters. The clear patterns of miRNA regulations in a disease are still elusive.

METHODS

In this work, we approach the miRNA-disease interactions from a network scientific perspective and devise two approaches - maximum weighted matching model (a graph theoretical algorithm which provides the result by solving an optimization equation of selecting the most prominent set of diseases) and motif-based analyses (which investigates the motifs of the miRNA-disease network and selects the most prominent set of diseases based on their maximum number of participation in motifs, thereby revealing the miRNA-disease interaction dynamics) to determine and prioritize the set of diseases which are most certainly impacted upon the activation of a group of queried miRNAs, in a miRNA-disease network.

RESULTS AND CONCLUSION

Our tool, DISMIRA implements the above mentioned approaches and presents an interactive visualization which helps the user in exploring the networking dynamics of miRNAs and diseases by analyzing their neighbors, paths and topological features. A set of miRNAs can be used in this analysis to get the associated diseases for the input group of miRs with ranks and also further analysis can be done to find key miRs or diseases, shortest paths etc. DISMIRA can be accessed online for free at http://bnet.egr.vcu.edu:8080/dismira.

摘要

背景

越来越多的研究发现，微小RNA（miRNA）在疾病调控中发挥着重要作用。鉴于一种miRNA可调控多达50种或更多疾病，且miRNA/疾病以簇的形式发挥作用，miRNA与疾病之间的相互作用是一个多层次相互作用的复杂网络。疾病中miRNA调控的清晰模式仍不明确。

方法

在本研究中，我们从网络科学的角度探讨miRNA与疾病之间的相互作用，并设计了两种方法——最大加权匹配模型（一种图论算法，通过求解选择最突出疾病集的优化方程得出结果）和基于基序的分析（研究miRNA-疾病网络的基序，并根据疾病参与基序的最大数量选择最突出的疾病集，从而揭示miRNA-疾病相互作用动态），以确定miRNA-疾病网络中一组查询miRNA激活时最可能受影响的疾病集，并对其进行优先级排序。

结果与结论

我们的工具DISMIRA实现了上述方法，并提供了一个交互式可视化界面，通过分析miRNA和疾病的邻居、路径和拓扑特征，帮助用户探索它们的网络动态。在该分析中，可以使用一组miRNA来获取输入miR组相关疾病的排名，还可以进一步分析以找到关键miR或疾病、最短路径等。可通过http://bnet.egr.vcu.edu:8080/dismira免费在线访问DISMIRA。

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