TY - JOUR T1 - Biomarker Discovery by Sparse Canonical Correlation Analysis of Complex Clinical Phenotypes of Tuberculosis and Malaria A1 - Rousu, Juho A1 - Agranoff, Daniel D. A1 - Sodeinde, Olugbemiro A1 - Shawe-Taylor, John A1 - Fernandez-Reyes, Delmiro Y1 - 2013/04/18 N2 - Author Summary Many infectious diseases such as tuberculosis and malaria are challenging both for scientists trying to understand the biochemical basis of the diseases and for medical doctors making diagnosis. The challenges arise both from the dependence of the diseases on sets of proteins and from the complexity of the symptoms. Biomarkers denote small sets of measurements that correlate with the phenotype of interest. They have potential use both in advancing the basic biomedical research of infectious diseases and in facilitating predictive diagnostic tools. We propose a new method for biomarker discovery that works by finding canonical correlations between two sets of data, the plasma proteomic profiles and clinical profiles of the subjects. We show that the method is able to find candidate proteomic biomarkers that correlate with combinations of clinical variables, called the clinical biomarkers. Using the clinical biomarkers improves the accuracy of diagnostic class prediction while not requiring the expensive plasma proteomic profiles to be measured for each subject. JF - PLOS Computational Biology JA - PLOS Computational Biology VL - 9 IS - 4 UR - https://doi.org/10.1371/journal.pcbi.1003018 SP - e1003018 EP - PB - Public Library of Science M3 - doi:10.1371/journal.pcbi.1003018 ER -