Applications of Bayesian network models in predicting types of hematological malignancies.
Applications of Bayesian network models in predicting types of hematological malignancies.
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DOI：
10.1038/s41598-018-24758-5
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发表时间：
2018-05-03
影响因子：
4.6
通讯作者：
Zare H
中科院分区：
文献类型：
--
作者：
Agrahari R;Foroushani A;Docking TR;Chang L;Duns G;Hudoba M;Karsan A;Zare H
网络分析是检测微妙但协调的变化在相互作用和相关的一组基因的表达的首选方法。我们介绍了一种基于共表达网络和贝叶斯网络分析的新方法，并使用这种新方法对两种类型的血液恶性肿瘤进行分类；即急性髓性白血病（AML）和骨髓增生异常综合征（MDS）。我们的分类器在训练数据集上的准确率为93%，精密度为98%，召回率为90% (n = 366)；这比其他学者在相同数据集上报告的结果要好。虽然我们的训练数据集由微阵列数据组成，但我们的模型在RNA-Seq测试数据集上表现出色（n = 74，准确率= 89%，精度= 88%，召回率= 98%），这证实了特征基因在表达谱技术方面是鲁棒的。这些特征对分类和正确预测诊断是有用的。它们还可能提供有关疾病潜在生物学的宝贵信息。我们的网络分析方法具有通用性，可用于基于基因表达谱对其他疾病进行分类。我们之前发表的Pigengene包可以通过Bioconductor公开获得，它可以方便地用于将贝叶斯网络拟合到基因表达数据中。
Network analysis is the preferred approach for the detection of subtle but coordinated changes in expression of an interacting and related set of genes. We introduce a novel method based on the analyses of coexpression networks and Bayesian networks, and we use this new method to classify two types of hematological malignancies; namely, acute myeloid leukemia (AML) and myelodysplastic syndrome (MDS). Our classifier has an accuracy of 93%, a precision of 98%, and a recall of 90% on the training dataset (n = 366); which outperforms the results reported by other scholars on the same dataset. Although our training dataset consists of microarray data, our model has a remarkable performance on the RNA-Seq test dataset (n = 74, accuracy = 89%, precision = 88%, recall = 98%), which confirms that eigengenes are robust with respect to expression profiling technology. These signatures are useful in classification and correctly predicting the diagnosis. They might also provide valuable information about the underlying biology of diseases. Our network analysis approach is generalizable and can be useful for classifying other diseases based on gene expression profiles. Our previously published Pigengene package is publicly available through Bioconductor, which can be used to conveniently fit a Bayesian network to gene expression data.
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