Grammatical-Restrained Hidden Conditional Random Fields for Bioinformatics applications

Table 1 Prediction of the topology of the Prokaryotic outer membrane proteins.

Method	POV	Q2	C(t)	Sn(t)	Sp(t)
CRF-1 (Vit)	0.26 ± 0.05	0.72 ± 0.01	0.47 ± 0.02	0.59 ± 0.01	0.80 ± 0.01
CRF-1 (Pvit)	0.39 ± 0.05	0.77 ± 0.01	0.54 ± 0.02	0.71 ± 0.01	0.80 ± 0.01
CRF-2 (Vit)	0.34 ± 0.05	0.76 ± 0.01	0.52 ± 0.03	0.63 ± 0.02	0.82 ± 0.02
CRF-2 (Pvit)	0.47 ± 0.05	0.80 ± 0.01	0.60 ± 0.03	0.74 ± 0.02	0.82 ± 0.02
CRF-3 (Vit)	0.29 ± 0.04	0.72 ± 0.01	0.45 ± 0.02	0.60 ± 0.02	0.79 ± 0.01
CRF-3 (Pvit)	0.45 ± 0.04	0.76 ± 0.01	0.52 ± 0.02	0.70 ± 0.02	0.79 ± 0.01
GRHCRF	0.66 ± 0.04	0.85 ± 0.01	0.70 ± 0.03	0.83 ± 0.01	0.84 ± 0.01
HMM-B2TMR	0.58 ± 0.04	0.80 ± 0.01	0.62 ± 0.02	0.82 ± 0.02	0.83 ± 0.01

C(t), Sn(t) and Sp(t) are reported for the transmembrane segments (t).
Vit = Viterbi decoding, Pvit = posterior-Viterbi decoding.
For GRHCRF and HMM-B2TMR we used the posterior-Viterbi decoding.
Models are detailed in the text. Scoring indices are described in Measure of Accuracy section.

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