title: Compound Identification Using Penalized Linear Regression on Metabolomics.
abstract: Compound identification is often achieved by matching the experimental mass spectra to the mass spectra stored in a reference library based on mass spectral similarity. Because the number of compounds in the reference library is much larger than the range of mass-to-charge ratio (m/z) values so that the data become high dimensional data suffering from singularity. For this reason, penalized linear regressions such as ridge regression and the lasso are used instead of the ordinary least squares regression. Furthermore, two-step approaches using the dot product and Pearson's correlation along with the penalized linear regression are proposed in this study.
authors.affiliation: Department of Bioinformatics and Biostatistics, University of Louisville, Louisville, KY.
authors.forename: Ruiqi
authors.initials: R
authors.lastname: Liu
authors.affiliation: Department of Bioinformatics and Biostatistics, University of Louisville, Louisville, KY.
authors.forename: Dongfeng
authors.initials: D
authors.lastname: Wu
authors.affiliation: Department of Chemistry, University of Louisville, Louisville, KY.
authors.forename: Xiang
authors.initials: X
authors.lastname: Zhang
authors.affiliation: Biostatistics Core, Karmanos Cancer Institute, Department of Oncology, Wayne State University School of Medicine, Detroit, MI.
authors.forename: Seongho
authors.initials: S
authors.lastname: Kim
keywords: Compound identification; Mass spectral similarity; Metabolomics; Penalized linear regression