TY - JOUR
T1 - Applications of Bayesian network models in predicting types of hematological malignancies
AU - Agrahari, Rupesh
AU - Foroushani, Amir
AU - Docking, T. Roderick
AU - Chang, Linda
AU - Duns, Gerben
AU - Hudoba, Monika
AU - Karsan, Aly
AU - Zare, Habil
N1 - Funding Information:
We would like to thank the GSC sequencing and library generation teams for sequencing. Work in the lab of A.K. was funded by grants from the Terry Fox Research Institute (122869), Canadian Institutes of Health Research (CIHR: MOP-133455 and MOP-97744), and Genome BC (121AML). A.K. is supported by the John Auston BC Cancer Foundation Clinical Investigator award. Work in the lab of H.Z. was supported by an internal grant from Texas State University. We thank Texas State University for providing the Graduate College Thesis Support Fellowship to R.A. We acknowledge the Texas Advanced Computing Center (TACC) at The University of Texas at Austin for providing high-performance computing (HPC) resources: http://www.tacc.utexas.edu. We thank Vincente LeCornu for proofreading the paper.
PY - 2018/12/1
Y1 - 2018/12/1
N2 - 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.
AB - 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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U2 - 10.1038/s41598-018-24758-5
DO - 10.1038/s41598-018-24758-5
M3 - Article
C2 - 29725024
AN - SCOPUS:85046532091
VL - 8
JO - Scientific Reports
JF - Scientific Reports
SN - 2045-2322
IS - 1
M1 - 6951
ER -