title: A comparative study on filtering protein secondary structure prediction.
abstract: Filtering of Protein Secondary Structure Prediction (PSSP) aims to provide physicochemically realistic results, while it usually improves the predictive performance. We performed a comparative study on this challenging problem, utilizing both machine learning techniques and empirical rules and we found that combinations of the two lead to the highest improvement.
authors.affiliation: Department of Computer Science, University of Cyprus, 75 Kallipoleos Avenue, PO Box 20537, 1678 Nicosia, Cyprus. kountour@cs.ucy.ac.cy
authors.forename: Petros
authors.initials: P
authors.lastname: Kountouris
authors.forename: Michalis
authors.initials: M
authors.lastname: Agathocleous
authors.forename: Vasilis J
authors.initials: VJ
authors.lastname: Promponas
authors.forename: Georgia
authors.initials: G
authors.lastname: Christodoulou
authors.forename: Simos
authors.initials: S
authors.lastname: Hadjicostas
authors.forename: Vassilis
authors.initials: V
authors.lastname: Vassiliades
authors.forename: Chris
authors.initials: C
authors.lastname: Christodoulou
mesh_terms: D000818:Animals; D001185:Artificial Intelligence; D030562:Databases, Protein; D006801:Humans; D017433:Protein Structure, Secondary; D011506:Proteins / Q000737:chemistry*
chemical_list: D011506:Proteins