title: DROP: an SVM domain linker predictor trained with optimal features selected by random forest.
abstract: BACKGROUND
  Biologically important proteins are often large, multidomain proteins, which are difficult to characterize by high-throughput experimental methods. Efficient domain/boundary predictions are thus increasingly required in diverse area of proteomics research for computationally dissecting proteins into readily analyzable domains.
  
  
  RESULTS
  We constructed a support vector machine (SVM)-based domain linker predictor, DROP (Domain linker pRediction using OPtimal features), which was trained with 25 optimal features. The optimal combination of features was identified from a set of 3000 features using a random forest algorithm complemented with a stepwise feature selection. DROP demonstrated a prediction sensitivity and precision of 41.3 and 49.4%, respectively. These values were over 19.9% higher than those of control SVM predictors trained with non-optimized features, strongly suggesting the efficiency of our feature selection method. In addition, the mean NDO-Score of DROP for predicting novel domains in seven CASP8 FM multidomain proteins was 0.760, which was higher than any of the 12 published CASP8 DP servers. Overall, these results indicate that the SVM prediction of domain linkers can be improved by identifying optimal features that best distinguish linker from non-linker regions.
authors.affiliation: Department of Biotechnology and Life Science, Tokyo University of Agriculture and Technology, Koganei-shi, Tokyo 184-8588, Japan.
authors.forename: Teppei
authors.initials: T
authors.lastname: Ebina
authors.forename: Hiroyuki
authors.initials: H
authors.lastname: Toh
authors.forename: Yutaka
authors.initials: Y
authors.lastname: Kuroda
mesh_terms: D000465:Algorithms; D019295:Computational Biology; D017433:Protein Structure, Secondary; D011506:Proteins / Q000032:analysis*; D040901:Proteomics / Q000379:methods*
chemical_list: D011506:Proteins