title: Achieving high accuracy prediction of minimotifs.
abstract: The low complexity of minimotif patterns results in a high false-positive prediction rate, hampering protein function prediction. A multi-filter algorithm, trained and tested on a linear regression model, support vector machine model, and neural network model, using a large dataset of verified minimotifs, vastly improves minimotif prediction accuracy while generating few false positives. An optimal threshold for the best accuracy reaches an overall accuracy above 90%, while a stringent threshold for the best specificity generates less than 1% false positives or even no false positives and still produces more than 90% true positives for the linear regression and neural network models. The minimotif multi-filter with its excellent accuracy represents the state-of-the-art in minimotif prediction and is expected to be very useful to biologists investigating protein function and how missense mutations cause disease.
authors.affiliation: Department of Computer Science and Engineering, University of Connecticut, Storrs, CT, USA.
authors.forename: Tian
authors.initials: T
authors.lastname: Mi
authors.forename: Sanguthevar
authors.initials: S
authors.lastname: Rajasekaran
authors.forename: Jerlin Camilus
authors.initials: JC
authors.lastname: Merlin
authors.forename: Michael
authors.initials: M
authors.lastname: Gryk
authors.forename: Martin R
authors.initials: MR
authors.lastname: Schiller
mesh_terms: D000465:Algorithms; D020816:Amino Acid Motifs; D019295:Computational Biology / Q000379:methods; D020407:Internet; D008962:Models, Theoretical; D010363:Pattern Recognition, Automated / Q000379:methods*; D011485:Protein Binding; D011506:Proteins / Q000737:chemistry*; D012372:ROC Curve
chemical_list: D011506:Proteins