Modelling of Oral Cancer Progression Using Dynamic Bayesian Networks (original) (raw)
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IEEE Journal of Biomedical and Health Informatics, 2017
Oral Squamous Cell Carcinoma (OSCC) has been characterized as a complex disease which involves dynamic genomic changes at the molecular level. These changes indicate the worth to explore the interactions of the molecules and especially of differentially expressed genes that contribute to cancer progression. Moreover, based on this knowledge the identification of differentially expressed genes and related molecular pathways is of great importance. In the present study we exploit differentially expressed genes in order to further perform pathway enrichment analysis. According to our results we found significant pathways in which the disease associated genes have been identified as strongly enriched. Furthermore, based on the results of the pathway enrichment analysis we propose a methodology for predicting oral cancer recurrence using Dynamic Bayesian Networks. The methodology takes into consideration time series gene expression data in order to predict a disease recurrence. Subsequently, we are able to conjecture about the causal interactions between genes in consecutive time intervals. Concerning the performance of the predictive models, the overall accuracy of the algorithm is 81.8% and the area under the ROC curve 89.2% regarding the knowledge from the overrepresented Pre-NOTCH Expression and Processing pathway. Index Terms-differentially expressed genes, Dynamic Bayesian network models, oral cancer recurrence prediction, pathway enrichment analysis I. INTRODUCTION T is generally argued that cancer is a disease characterized by abnormal cells growth that invades healthy tissues in the body. The last decade, rapid advances in cancer research community revealed that cancer is a complex disease with fluctuations in gene expression process at the molecular level.
Bayesian correction for mortality trend of oral cavity cancer
Gastroenterology and Hepatology From Bed to Bench, 2012
Aim The aim of this study is to estimate oral cavity cancer mortality for Iranian population, using Bayesian approach in order to revise this misclassification. Background Mortality is a familiar projection to address the burden of cancers, but according to Iranian death registry, about 20% death statistics were still recorded in misclassified categories. Patients and methods We analyzed national death statistic reported by the Iranian Ministry of Health and Medical Education from 1995 to 2004 stratified by age group, sex, and cause of death are included in this analysis. Oral cavity cancer [ICD-10; C00-08] were expressed as the annual mortality rates/100,000, overall, by sex and by age group (<50 and ≥50 years of age) and age standardized rate (ASR). The Bayesian approach to correct and account for misclassification effects in Poisson count regression with a beta prior employed to estimate the mortality rate of EC in age and sex group. Results According to the Bayesian re-estima...
Unification of heterogeneous data towards the prediction of oral cancer reoccurrence
2009
Oral cancer is the predominant neoplasm of the head and neck. Annually, more than 500.000 new cases of oral cancer are reported, worldwide. After the initial treatment of cancer and its complete disappearance, a state called remission, reoccurrence rates still remain quite high and the early identification of such relapses is a matter of great importance. Up to now, several approaches have been proposed for this purpose yielding however, unsatisfactory results. This is mainly attributed to the fragmented nature of these studies which took into account only a limited subset of the factors involved in the development and reoccurrence of oral cancer. In this work we propose a unified and orchestrated approach based on Dynamic Bayesian Networks (DBNs) for the prediction of oral cancer reoccurrence after the disease has reached remission. Several heterogeneous data sources featuring clinical, imaging and genomic information are assembled and analyzed over time, in order to procure new and informative biomarkers which correlate with the progression of the disease and identify early potential relapses (local or metastatic) of the disease.