An efficient gene expression data classification using optimized bidirectional long short-term memory with self attention mechanism

In recent years, DNA microarray is a recent research technique aimed at classifying gene expression. This paper is to develop Optimal Bidirectional Long Short-Term Memory with Self Attention Mechanism (OBiLSTM-SAM) for gene expression data classification. In the initial phase, the database is gathered from online sources. The projected technique is proceeding with three stages pre-processing, Gene extraction, and classification. In the pre-processing phase, the MIN-MAX normalization techniques are considered. After that, gene extraction is achieved with the consideration of Mutual Information Maximization (MIM). The extracted data will be utilized aimed at gene expression data classification. In the classification stage, the OBiLSTM-SAM is utilized. The OBiLSTM-SAM is a grouping of Bidirectional Long Short-Term Memory with Self Attention Mechanism (BiLSTM-SAM) and Remora Optimization Algorithm (ROA). In the BiLSTM-SAM, the weight parameters are selected with the assistance of ROA. Based on the ODSNN, gene expression data classification is achieved. The presentation of the proposed methodology is evaluated in f-measure, recall, precision, specificity, sensitivity, and accuracy. To validate the projected technique, it is compared with optimized long short-term memory, algorithm (OLSTM) and Adaptive Salp Swarm Optimization algorithm (ASSA) respectively.