Cancer MiRNA biomarker classification based on Improved Generative Adversarial Network optimized with Mayfly Optimization Algorithm. (May 2022) - Tamilmani, G.; Devi, V. Brindha; Sujithra, T.; Shajin, Francis H.; Rajesh, P.
Highlights: Cancer diagnosis becomes a paradigm shift by incorporating molecular biomarkers as part of a routine diagnostic panel. Ranges of molecular changes include DNA, RNA, micro RNA (miRNAs) and proteins. IGAN optimized with Mayfly Optimization Algorithm is proposed to overcome the super class issues. The DCG is used to balance the dataset by creating more samples in the training dataset. The proposed method is activated in python and its efficiency is analyzed with Cancer Genome Atlas dataset. Abstract: Nowadays, cancer diagnosis becomes a paradigm shift by incorporating molecular biomarkers as part of a routine diagnostic panel. Ranges of molecular changes include DNA, RNA, micro RNA (miRNAs) and proteins. In recent years, deep learning based methods have been more inspired to health researcher's regarding the performance of cancer diagnosis. The application of deep learning-based approach gradually becomes clearer in classification accuracy for a problem that separates data related to cancer survival. In this manuscript, an Improved Generative Adversarial Network optimized with Mayfly Optimization Algorithm is proposed to overcome the super class issues. Improved Generative Adversarial Network is the combination of deep convolutional generative adversarial network (DCG) and modified convolutional neural network (MCNN); hence it is called DCG-MCNN. Initially, the DCG is used to balance the dataset by creating more samples in the training dataset. Based on the training dataset, cancer miRNA biomarker classification is improved with the help of modified CNN diagnosis model. The proposed method is activated in python, moreover, its efficiency is analyzed with Cancer Genome Atlas dataset. Here, performance metrics, viz accuracy, sensitivity, specificity, precision, F-measure balanced error rate are calculated. The experimental results of the proposed method shows higher accuracy 99.26%, higher sensitivity 95.23%, higher specificity 92.56% compared with the existing methods, like Validation of miRNAs as breast cancer biomarkers with a machine learning approach (CMiRNA-BC-RF-SVM), Cancer miRNA biomarkers classification using a new representation algorithm and evolutionary deep learning (CMiRNA-BC-CNN) and multi-omics data using graph convolutional networks allowing patient classification and biomarker identification (CMiRNA-BC-GCNN).