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A path-based computational model for long non-coding RNA-protein interaction prediction

机译：长非编码RNA蛋白相互作用预测的基于路径的计算模型

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摘要

Recently, lncRNAs have attracted accumulating attentions because more and more experimental researches have shown lncRNA can play critical roles in many biological processes. Predicting potential interactions between lncRNAs and proteins are key to understand the lncRNAs biological functions. But traditional biological experiments are expensive and time-consuming, network similarity methods provide a powerful solution to computationally predict lncRNA-protein interactions. In this work, a novel path-based lncRNA-protein interaction (PBLPI) prediction model is proposed by integrating protein semantic similarity, lncRNA functional similarity, known human lncRNA-protein interactions, and Gaussian interaction profile kernel similarity. PBLPI model utilizes three interlinked sub-graphs to construct a heterogeneous graph, and then infers potential lncRNA-protein interactions through depth-first search algorithm. Consequently, PBLPI achieves reliable performance in the frameworks of 5-fold cross validation (average AUC is 0.9244 and AUPR is 0.6478). In the case study, we use “Mus musculus” data to further validate the reliability of PBLPI method. It is anticipated that PBLPI would become a useful tool to identify potential lncRNA-protein interactions.
机译：最近，LNCRNA吸引了积累的关注，因为越来越多的实验研究已经显示出LNCRNA可以在许多生物过程中起重要作用。预测LNCRNA和蛋白质之间的潜在相互作用是理解LNCRNA生物学功能的关键。但传统的生物实验是昂贵且耗时的，网络相似性方法提供了一种强大的解决方案来计算预测LNCRNA-蛋白质相互作用。在这项工作中，通过整合蛋白质语义相似性，LNCRNA官能相似性，已知的人LNCRNA - 蛋白质相互作用和高斯相互作用核相似性提出了一种新的基于路径的LNCRNA - 蛋白质相互作用（PBLPI）预测模型。 PBLPI模型利用三个相互关联的子图构建异质图，然后通过深度第一搜索算法递送潜在的LNCRNA-蛋白相互作用。因此，PBLPI在5倍交叉验证的框架中实现了可靠的性能（平均AUC为0.9244，AUPR为0.6478）。在案例研究中，我们使用“Mus Musculus”数据来进一步验证PBLPI方法的可靠性。预计PBLPI将成为识别潜在的LNCRNA蛋白质相互作用的有用工具。

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