Integrated Transcriptomic Meta-analysis And Comparative Artificial Intelligence Models In Maize Under Biotic Stress

This study explores the use of machine learning and deep learning algorithms to identify genes expressed under normal and biotic stress conditions in maize. The machine learning algorithms used are Naive Bayes (NB), K-Nearest Neighbor (KNN), Ensemble, Support Vector Machine (SVM), and Decision Tree (DT). A Bidirectional Long Short Term Memory (BiLSTM) based network with Recurrent Neural Network (RNN) architecture is proposed for gene classification with deep learning. Feature selection is made from the raw gene features through the Relief feature selection algorithm. The results showed that BiLSTM was more effective than other machine learning algorithms. Some top genes were found to be differentially upregulated under biotic stress condition.