Unraveling the Molecular Landscape of Myasthenia Gravis (MG): A Principal Component Analysis (PCA) of Gene Expression Dataset

Myasthenia Gravis (MG) is a chronic neural disorder which affects the nervous system and muscles of the human body. As there is no cure for the disease, the early detection of the disease is vital to keep the patient steady for a longer time. Myasthenia Gravis is a rare disease, and its exact cause is not fully identified. However, it is believed to depend on a combination of genetic and other environmental factors of the patient. It is seen to be more common in women and tends to occur in younger women than the older women. In this paper, a machine learning algorithm, named Principal Component Analysis (PCA) has been applied to identify the genes which contribute to the disease. A set of important genes are marked as the identifiers that contribute significantly to cause Myasthenia Gravis disease. The samples of both diseased and normal humans have been considered to carry out the study. At the end of the study, 45 significant genes have been identified which seem to be responsible for causing the disease, Myasthenia Gravis.