ODLCP: A Novel Methodology to Predict Immune System Strength by Using Optimized Deep Learning and Classification Principle

Immune responses that are mediated by antibodies are essential to the functioning of the immune system in vertebrates. Due to the fact that antibodies have the ability to connect to antigens, they are employed widely in the treatment of cancer as well as other dangerous ailments. The process of experimentally finding antibody-antigen interactions is an essential stage in antibody therapy. This process is typically laborious, costly, and time-consuming. Because of the continued reliance on multidimensional structures, the deployment of computational methodologies that have been recommended for screening potential antibodies continues to be restricted. We introduce a unique deep learning strategy called Optimized Deep Learning and Classification Principle (ODLCP) for accurate immune strength prediction in this paper. In order to evaluate the effectiveness of this method, we cross-validate it with a more conventional learning method known as Convolutional Neural Network (CNN). By incorporating a wide range of clinical and immunological data, our goal is to develop a prediction model that is capable of accurately determining the strength of an individual's immune system. It is possible that the proposed paradigm would revolutionize healthcare since it will make it possible to develop individualized treatments that will improve immune health and allow for the early detection of disorders connected to immunological processes.