Comparison of some machine learning and statistical algorithms for classification and prediction of human cancer type

Use of gene expression profile of an animal under a certain disease gives pre-clinical insights for the potential efficacy of novel drugs. Selection of an animal model, accurately resembling the human disease, profoundly reduces the research cost in resources and time. Here, a statistical procedure based on analysis of variance (ANOVA) defined in [1] is investigated to select the animal model that most accurately mimics the human disease in terms of genome-wide gene expression. Two other commonly used data fitting algorithms in machine learning, logistic regression and artificial neural networks are examined and analyzed for the same data set. Implementing procedure of each of these algorithms is discussed and computational cost and advantage and drawback of each algorithm is scrutinized for prediction of pediatric Medulloblastoma cancer type.