Classification of patients with lithium-treated bipolar disorder based on gene expression: Dirichlet Bayesian network model

Abstract

Abstract Backgrounds: Dirichlet Bayesian network (DBN) model is a method with score-based structural learning, leading to a more accurate knowledge of the structure of the Bayesian network. Therefore, the DBN was used in this study to classify gene expression data in bipolar disorder (BD) with lithium treatment. Methods: In this study, gene expression data of patients with BD, including 47323 genes, were used, of which 30 received standard treatment and 30 received lithium treatment. The first essential variables were selected using partial least squares (PLS) regression to analyze and classify the data. The plaid algorithm was used to discover identical patterns and biclusters of gene expression data. We implemented principal component analysis (PCA) to represent a component for each bicluster. Then we created the DBN model toclassify the correlation network. Finally, the accuracy of the prediction model was evaluated using Receiver operating characteristic (ROC) curve analysis. R3.6.2 software was used to analyze the data. Results: In this analysis, the number of essential and significant genes discovered using PLS regression was 10788. We used the plaid algorithm and nine homogeneous biclusters were discovered. The representative component of the biclusters was selected with at least 75% of the variance in the data using PCA. Then the classification was performed using DBN which the model's accuracy was 0.86 and the model's precision was 0.91. Conclusions: This study demonstrates the potential of an ensemble approach, which can be developed for network analysis for thousands of genes. Combining models produces more robust and accurate models than single models. Also, network analysis is a desirable approach to detect subtle but coordinated changes in the mutual and related expression of a set of genes. This method can help study other diseases using existing datasets.