Statistical Mixture-Based Methods and Computational Tools for High-Throughput Data Derived in Proteomics and Metabolomics Study

Abstract Qatar is accumulating substantial local expertise in biomedical data analytics. In particular, QCRI is forming a scientific computing multidisciplinary group with a particular interest in machine learning, statistical modeling and bioinformatics. We are now in a strong position to address the computational needs of biomedical researchers in Qatar, and to prepare a new generation of scientists with a multidisciplinary expertise. The goal of genomics, proteomics and metabolomics is to identify, and characterize the function of genes, proteins, and small molecules that participate in chemical reactions, and are essential for maintaining life. This research area expands rapidly and holds a great promise in the discovery of risk factors and potential biomarkers of diseases such as obesity and diabetes, the two areas of increasing concern in Qatar population. In this paper, we develop new statistical modeling techniques of clustering based on mixture models with model selection of large biomedical data...