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Table 1 Results obtained from the various predictors, on a dataset of 72 transmembrane proteins [38]. Results obtained when the methods were not trained and tested on the same dataset, however some of the proteins in the dataset were present in the datasets used for training the other methods. The results of HMM-TM were obtained through a nine-fold cross validation procedure. The methods that allow the incorporation of experimental information are listed separately. The results of UMDHMMTMHP could not be obtained by cross-validation (since it was trained on the same dataset), and thus are listed separately in the text

From: Algorithms for incorporating prior topological information in HMMs: application to transmembrane proteins

Method

Q

C

SOV

Correctly predicted TM segments (%)

Correctly predicted Topologies (%)

Methods that allow the incorporation of experimental information

     

HMM-TM (cross-validation)

0.903

0.762

0.939

59/72 (81.9%)

55/72 (76.4%)

TMHMM

0.902

0.762

0.931

58/72 (80.6%)

49/72 (68.1%)

HMMTOP

0.890

0.735

0.932

58/72 (80.6%)

49/72 (68.1%)

Phobius †

0.911

0.785

0.954

65/72 (90.3%)

52/72 (72.2%)

Methods that do not allow the incorporation of experimental information

     

MEMSAT

0.905

0.767

0.954

63/72 (87.5%)

48/72 (66.7%)

S-TMHMM †

0.897

0.747

0.925

59/72 (81.9%)

52/72 (72.2%)

PRO-TMHMM* †

0.910

0.779

0.945

65/72 (90.3%)

63/72 (87.5%)

PRODIV-TMHMM* †

0.914

0.794

0.970

67/72 (93.1%)

64/72 (87.5%)

  1. * The methods using evolutionary information are denoted with an asterisk.
  2. † These predictors were trained on sets containing sequences similar to the ones included in the training set we used here