A Review on Anisotropic Anomaly based Cancer- Causing Gene Detection using Hybrid ML Techniques

Cancer refers to a set of illnesses brought on by uncontrollable cell development that can affect various body organs. According to the World Health Organization (WHO), cancer is the second leading cause of death after cardiovascular illnesses. Gene expression can play a critical role in the early detection of cancer since it can provide information about an organism's genetic composition as well as the biochemical processes occurring in tissue and cells. This study presents a review on cancer causing gene detection based on dataset analysis, Hybrid Radiomics solutions-based DL model, semi-supervised ensemble learning, Anisotropic Anomaly-based Gene detection techniques, early diagnosis of cancer causing genes, and performance analysis of various cancer causing gene techniques. This review provides a clear idea of strategies used in cancer causing gene detection. Additionally, it offers a comparison study to show the efficacy of different methods in cancer-causing gene discovery methodologies. Moreover, future directions are provided to overcome the existing impact of various techniques in the detection of cancer causing genes.