Applications of Bayesian network models in predicting types of hematological malignancies
Autor: | T. Roderick Docking, Aly Karsan, Amir Foroushani, Rupesh Agrahari, Monika Hudoba, Gerben Duns, Habil Zare, Linda Chang |
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Jazyk: | angličtina |
Rok vydání: | 2018 |
Předmět: |
0301 basic medicine
Computer science Gene regulatory network lcsh:Medicine Machine learning computer.software_genre Article Bioconductor 03 medical and health sciences Bayes' theorem Gene expression Humans Gene Regulatory Networks lcsh:Science Gene Multidisciplinary business.industry Microarray analysis techniques Sequence Analysis RNA lcsh:R Bayesian network Bayes Theorem 3. Good health Gene expression profiling Gene Expression Regulation Neoplastic Leukemia Myeloid Acute 030104 developmental biology Hematologic Neoplasms Myelodysplastic Syndromes lcsh:Q Artificial intelligence business Transcriptome Classifier (UML) computer |
Zdroj: | Scientific Reports, Vol 8, Iss 1, Pp 1-12 (2018) Scientific Reports |
ISSN: | 2045-2322 |
Popis: | 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. |
Databáze: | OpenAIRE |
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