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Incorporating K-mers Highly Correlated to Epigenetic Modifications for Bayesian Inference of Gene Interactions
Current Bioinformatics ( IF 4 ) Pub Date : 2021-02-28 , DOI: 10.2174/1574893615999200728193621
Dariush Salimi 1 , Ali Moeini 2
Affiliation  

Objective: A gene interaction network, along with its related biological features, has an important role in computational biology. Bayesian network, as an efficient model, based on probabilistic concepts is able to exploit known and novel biological casual relationships between genes. The success of Bayesian networks in predicting the relationships greatly depends on selecting priors.

Methods: K-mers have been applied as the prominent features to uncover the similarity between genes in a specific pathway, suggesting that this feature can be applied to study genes dependencies. In this study, we propose k-mers (4,5 and 6-mers) highly correlated with epigenetic modifications, including 17 modifications, as a new prior for Bayesian inference in the gene interaction network.

Result: Employing this model on a network of 23 human genes and on a network based on 27 genes related to yeast resulted in F-measure improvements in different biological networks.

Conclusion: The improvements in the best case are 12%, 36%, and 10% in the pathway, coexpression, and physical interaction, respectively.



中文翻译:

纳入与表观遗传修饰高度相关的K-mers,以进行基因相互作用的贝叶斯推断。

目的:基因相互作用网络及其相关的生物学特征在计算生物学中具有重要作用。贝叶斯网络作为一种基于概率概念的有效模型,能够利用基因之间已知的和新颖的生物随意关系。贝叶斯网络在预测关系方面的成功很大程度上取决于选择先验条件。

方法:已将K-mers用作突出特征,以揭示特定途径中基因之间的相似性,表明该特征可用于研究基因依赖性。在这项研究中,我们提出与表观遗传修饰高度相关的k-mers(4,5和6-mers),包括17个修饰,作为基因相互作用网络中贝叶斯推断的新先验。

结果:在包含23个人类基因的网络和基于与酵母相关的27个基因的网络上使用此模型,可以在不同的生物网络中改进F-measure。

结论:在最佳情况下,通路,共表达和身体相互作用的改善分别为12%,36%和10%。

更新日期:2021-02-28
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