BiPoP: Bipolar Disorder Optimized Preprocessing Framework for Stress Disorder Identification through Gene Expression Data using Deep Learning

Gene expression data are widely used in diagnosing diseases and identifying promising genes with the advancement in computational tools in biology. Gene Expression Omnibus (GEO) datasets provide the gene expression data for various diseases and disorders. For Bipolar Disorder, GSE46449 was obtained from the NCBI data repository. This study aimed to classify control (Normal) and case (Disordered) individuals from samples using Machine Learning (ML)/Deep Learning (DL) models. The preprocessing involved the removal of null values and normalization of gene expression values using R. The second step focussed on the selection of optimal features/genes from the gene expression dataset. The Pearson Correlation Coefficient (PCC) along with Principal Component Analysis (PCA) were used for feature selection. The samples were then classified using ML/DL models. A Multi-Layer Perceptron (MLP) was used to validate the optimal feature set to classify healthy and disordered individuals. The proposed Bipolar Disorder Preprocessing Framework (BiPoP) was validated for its targeted use, highlighting its multifunctional and fine-tuned approach to preprocessing and achieving a classification accuracy of 98.9%.