title: Computational classification of classically secreted proteins.
abstract: The ability to identify classically secreted proteins is an important component of targeted therapeutic studies and the discovery of circulating biomarkers. Here, we review some of the most recent programs available for the in silico prediction of secretory proteins, the performance of which is benchmarked with an independent set of annotated human proteins. The description of these programs and the results of this benchmarking provide insights into the most recently developed prediction programs, which will enable investigators to make more informed decisions about which program best addresses their research needs.
authors.affiliation: Stabile 3-15, Department of Laboratory Medicine, Mayo Clinic, 200 First Street SW, Rochester, MN 55905, USA.
authors.forename: Eric W
authors.initials: EW
authors.lastname: Klee
authors.forename: Carlos P
authors.initials: CP
authors.lastname: Sosa
mesh_terms: D000465:Algorithms; D019985:Benchmarking; D015415:Biomarkers; D019295:Computational Biology; D003198:Computer Simulation; D017076:Computer-Aided Design; D003657:Decision Making; D016503:Drug Delivery Systems; D015195:Drug Design; D005544:Forecasting; D006801:Humans; D011506:Proteins / Q000145:classification* / Q000378:metabolism / Q000502:physiology; D012984:Software; D013347:Subcellular Fractions
chemical_list: D015415:Biomarkers; D011506:Proteins