Significant Metabolites and Outlier-Robust Classifier Identification for Breast Cancer Prediction

Background: Metabolomics is a relatively new and dominant branch of bioinformatics. Metabolite expression level controls the phenotypic characteristics of any organism. Recently, breast cancer is the leading type of cancer in women across the world, accounting for 25% of all cases. In 2012, it was seen that due to breast cancer, there were 1.68 million cases and 522,000 deaths. Therefore, for drug discovery as well as for early disease status prediction, significant metabolites identification for breast cancer and correct classification of the breast cancer status through classification technique are very important for metabolomics data analysis. Objective: The main objective of this paper is to identify significant metabolites (p-value<0.05) and state of the art classification technique for breast cancer prediction using…