title: Protein family classification with partial least squares.
abstract: The quality of protein function predictions relies on appropriate training of protein classification methods. Performance of these methods can be affected when only a limited number of protein samples are available, which is often the case in divergent protein families. Whereas profile hidden Markov models and PSI-BLAST presented significant performance decrease in such cases, alignment-free partial least-squares classifiers performed consistently better even when used to identify short fragmented sequences.
authors.affiliation: Department of Agronomy and Horticulture, University of Nebraska-Lincoln, Lincoln, NE 68583-0915, USA.
authors.forename: Stephen O
authors.initials: SO
authors.lastname: Opiyo
authors.forename: Etsuko N
authors.initials: EN
authors.lastname: Moriyama
mesh_terms: D000595:Amino Acid Sequence; D000596:Amino Acids / Q000032:analysis; D000818:Animals; D029681:Arabidopsis Proteins / Q000737:chemistry; D016018:Least-Squares Analysis; D008390:Markov Chains; D010940:Plant Proteins / Q000737:chemistry; D011237:Predictive Value of Tests; D043562:Receptors, G-Protein-Coupled / Q000737:chemistry* / Q000145:classification*; D015203:Reproducibility of Results
chemical_list: D000596:Amino Acids; D029681:Arabidopsis Proteins; D010940:Plant Proteins; D043562:Receptors, G-Protein-Coupled