Al-Aamri Amira, Taha Kamal, Maalouf Maher, Kudlicki Andrzej, Homouz Dirar
Department of Biomedical Engineering, Khalifa University of Science and Technology, Abu Dhabi, UAE.
Department of Electrical and Computer Engineering, Khalifa University of Science and Technology, Abu Dhabi, UAE.
Evol Bioinform Online. 2020 Jun 24;16:1176934320920310. doi: 10.1177/1176934320920310. eCollection 2020.
Computational prediction of gene-gene associations is one of the productive directions in the study of bioinformatics. Many tools are developed to infer the relation between genes using different biological data sources. The association of a pair of genes deduced from the analysis of biological data becomes meaningful when it reflects the directionality and the type of reaction between genes. In this work, we follow another method to construct a causal gene co-expression network while identifying transcription factors in each pair of genes using microarray expression data. We adopt a machine learning technique based on a logistic regression model to tackle the sparsity of the network and to improve the quality of the prediction accuracy. The proposed system classifies each pair of genes into either connected or nonconnected class using the data of the correlation between these genes in the whole genome. The accuracy of the classification model in predicting related genes was evaluated using several data sets for the yeast regulatory network. Our system achieves high performance in terms of several statistical measures.
基因-基因关联的计算预测是生物信息学研究中的一个富有成效的方向。人们开发了许多工具，利用不同的生物数据源来推断基因之间的关系。当从生物数据分析中推导出来的一对基因的关联反映了基因之间的方向性和反应类型时，它就变得有意义了。在这项工作中，我们采用另一种方法来构建因果基因共表达网络，同时使用微阵列表达数据识别每对基因中的转录因子。我们采用基于逻辑回归模型的机器学习技术来解决网络的稀疏性问题，并提高预测准确性的质量。所提出的系统使用全基因组中这些基因之间的相关性数据，将每对基因分类为连接或非连接类别。使用酵母调控网络的几个数据集评估了分类模型在预测相关基因方面的准确性。我们的系统在几个统计指标方面都取得了高性能。
