Optimized twin spatio-temporal convolutional neural network with squeezenet transfer learning model for cancer miRNA biomarker classification

Abstract

Cancer MicroRNA (miRNA) biomarkers are small RNA molecules identified in cancer cells that indicate the presence or progression of cancer. miRNA is detected through circulating (liquid biopsy) and tissue-based approaches. Handling the data involves sequencing, followed by bioinformatic analysis to interpret the miRNA profiles. The valuable knowledge is extracted from the cancer genomic datasets using data mining and machine learning techniques, although the accuracy of certain classes has been unsatisfied. To address this issue, an optimized twin spatio-temporal convolutional neural network with SqueezeNet transfer learning model for cancer miRNA biomarker classification (TSTCNN-SNet-AGT-CBC) is proposed. The input imageries are gathered from Cancer Genome Atlas dataset. The noise is reduced and image quality is improved in preprocessing by applying Reliable asynchronous sampled-data filtering (RASDF). Then, features are extracted and the cancer miRNA is effectively classified as a diagnosis, therapy, and prognostic normal region using twin spatio-temporal convolutional neural network with SqueezeNet. The batch normalization layer of the twin Spatio-Temporal Convolutional Neural Network (TSTCNN) is eliminated and added with Squeeze Net Layer (SNet). Then, the Artificial Gorilla Troops Optimization (AGTO) is used for tuning the TSTCNN-SNet optimum weight parameter. The performance of proposed technique is examined under performance metrics like precision, sensitivity, F-scores and accuracy. The proposed TSTCNN-SNet-AGT-CBC method attains 24.52 %, 20.97 % and 16.36 % higher accuracy and 23.68 %, 18.77 %, and 41.36 % greater precision when compared with existing techniques.