title: Improved disorder prediction by combination of orthogonal approaches.
abstract: Disordered proteins are highly abundant in regulatory processes such as transcription and cell-signaling. Different methods have been developed to predict protein disorder often focusing on different types of disordered regions. Here, we present MD, a novel META-Disorder prediction method that molds various sources of information predominantly obtained from orthogonal prediction methods, to significantly improve in performance over its constituents. In sustained cross-validation, MD not only outperforms its origins, but it also compares favorably to other state-of-the-art prediction methods in a variety of tests that we applied.
  
  
  BACKGROUND
  http://www.rostlab.org/services/md/
authors.affiliation: CUBIC, Department of Biochemistry and Molecular Biophysics, Columbia University, New York, New York, United States of America. as2067@columbia.edu
authors.forename: Avner
authors.initials: A
authors.lastname: Schlessinger
authors.forename: Marco
authors.initials: M
authors.lastname: Punta
authors.forename: Guy
authors.initials: G
authors.lastname: Yachdav
authors.forename: Laszlo
authors.initials: L
authors.lastname: Kajan
authors.forename: Burkhard
authors.initials: B
authors.lastname: Rost
mesh_terms: D019295:Computational Biology / Q000379:methods*; D030562:Databases, Protein; D008958:Models, Molecular; D011506:Proteins / Q000737:chemistry*; D015203:Reproducibility of Results
chemical_list: D011506:Proteins