title: Towards discovering structural signatures of protein folds based on logical hidden Markov models.
abstract: With the growing number of determined protein structures and the availability of classification schemes, it becomes increasingly important to develop computer methods that automatically extract structural signatures for classes of proteins. In this paper, we introduce and apply a new Machine Learning technique, Logical Hidden Markov Models (LOHMMs), to the task of finding structural signatures of folds according to the classification scheme SCOP. Our results indicate that LOHMMs are applicable to this task and possess several advantages over other approaches.
authors.affiliation: Institute for Computer Science, Machine Learning Lab, University of Freiburg, Georges-Koehler-Allee 079, 79112 Freiburg, Germany.
authors.forename: K
authors.initials: K
authors.lastname: Kersting
authors.forename: T
authors.initials: T
authors.lastname: Raiko
authors.forename: S
authors.initials: S
authors.lastname: Kramer
authors.forename: L
authors.initials: L
authors.lastname: De Raedt
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D030562:Databases, Protein; D008390:Markov Chains; D008958:Models, Molecular; D017510:Protein Folding; D017434:Protein Structure, Tertiary; D011506:Proteins / Q000737:chemistry*
chemical_list: D011506:Proteins