title: Sequence- and structure-based protein function prediction from genomic information.
abstract: Existing functional annotation transfer is fraught with inaccuracies that may hinder forward interpretation and mining of genomic data. Hand-curation of the annotation placed into databases is not practical. In lieu of experimental evidence, computational biological approaches offer high-throughput tools to predict function accurately; however, these methods are still notably deficient in defining and describing the complexity of protein function. Enriching genomic sequences obtained from sequencing efforts and expression array methods with protein function information and classification will be an efficient first step for incorporating genomic data into drug discovery programs.
authors.affiliation: GeneFormatics Inc, 5830 Oberlin Drive, Suite 200, San Diego, CA 92121, USA.
authors.forename: S M
authors.initials: SM
authors.lastname: Baxter
authors.forename: J S
authors.initials: JS
authors.lastname: Fetrow
mesh_terms: D019295:Computational Biology; D016208:Databases, Factual; D015698:Genomic Library; D008958:Models, Molecular; D011487:Protein Conformation; D011506:Proteins / Q000737:chemistry* / Q000235:genetics / Q000502:physiology*; D013329:Structure-Activity Relationship
chemical_list: D011506:Proteins