Applications of Bayesian network models in predicting types of hematological malignancies

Rupesh Agrahari, Amir Foroushani, T. Roderick Docking, Linda Chang, Gerben Duns, Monika Hudoba, Aly Karsan, Habil Zare

Research output: Contribution to journalArticle

9 Scopus citations

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 Pigengene package is publicly available through Bioconductor, which can be used to conveniently fit a Bayesian network to gene expression data.

Original languageEnglish (US)
Article number6951
JournalScientific reports
Volume8
Issue number1
DOIs
StatePublished - Dec 1 2018

ASJC Scopus subject areas

  • General

Fingerprint Dive into the research topics of 'Applications of Bayesian network models in predicting types of hematological malignancies'. Together they form a unique fingerprint.

  • Cite this

    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(1), [6951]. https://doi.org/10.1038/s41598-018-24758-5