Applications of Bayesian Network Models in Predicting Types of Hematological Malignancies
Date
2018-05
Authors
Agrahari, Rupesh
Foroushani, Amir
Docking, T. Roderick
Chang, Linda
Duns, Gerben
Hudoba, Monika
Karsan, Aly
Zare, Habil
Journal Title
Journal ISSN
Volume Title
Publisher
Nature Publishing Group
Abstract
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 <i>Pigengene</i> package is publicly available through Bioconductor, which can be used to conveniently fit a Bayesian network to gene expression data.
Description
Keywords
Bayesian network models, hematological malignancies, genes, Computer Science
Citation
Agrahari, R., Foroushani, A., Docking, T. R., Chang, L., Duns, G., Hudoba, M., Karsan, A., & Zare, H. (2018). Applications of Bayesian network models in predicting types of hematological malignancies. Scientific Reports, 8(6951).
Rights
Rights Holder
© 2018 The Author(s).
Rights License
This work is licensed under a Creative Commons Attribution 4.0 International License.