Gene expression analysis of transcriptomic data enables us to identify changes in gene expression under some biological conditions. Ribonucleic acid (RNA) sequencing (RNA-seq) data can show genetic mutations and intricate biological process connections, which are useful in the diagnosis and treatment of cancer. The existing classical differential gene expression analysis techniques are prone to false negatives and false positives with smaller datasets. With the improvements in the field of machine learning (ML), we want to build an ensemble learning model for the classification of differentially expressed genes (DEGs) from RNA-seq data for pancreatic cancer. The gene expression data was obtained from the Cancer Genome Atlas-Pancreatic Adenocarcinoma Project database. In this paper, we are proposing a stacking classifier with cross-validation called the stacking CV classifier, which is an ensemble of K-nearest neighbor, random forest, gradient boosting, and logistic regression classifiers for effective classification of DEGs. We also made a comparative analysis between the results of our ensemble model and existing models in the literature. The results of our model were competitive (accuracy 96% and area under the curve 0.99) against the stand-alone and existing gene classification models. Our ML-based model is a promising tool for classifying DEGs based on gene expression patterns.
Key words: RNA-seq, gene classification, pancreatic cancer, ensemble learning, machine learning, transcriptomics
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