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Table 1 Performance of HMM-d, HMM-t, HMM-ModE and HMM-Sub for the sub-family classification of the AGC family of kinases.

From: HMM-ModE – Improved classification using profile hidden Markov models by optimising the discrimination threshold and modifying emission probabilities with negative training sequences

Methods HMM-d HMM-T HMM-ModE HMM-Sub
Sub-groups of AGC kinases Sensitivity Specificity Sensitivity Specificity Sensitivity Specificity Sensitivity Specificity
GRK 1 (0) 0.27 (0.03) 1 (0) 1 (0) 1 (0) 1 (0) 1 (0) 1 (0)
PKA 0.96 (0.1) 0.18 (0.02) 0.89 (0.13) 1 (0) 0.89 (0.13) 1 (0) 0.96 (0.07) 1 (0)
PKC 0.99 (0.05) 0.42 (0.1) 0.95 (0.08) 0.99 (0.2) 0.96 (0.08) 1 (0) 0.97 (0.06) 1 (0)
PVPK 1 (0) 0.17 (0.03) 0.94 (0.1) 0.93 (0.13) 0.94 (0.1) 0.96 (0.12) 0.96 (0.08) 1 (0)
RAC 1 (0) 0.09 (0.00) 0.9 (0.16) 1 (0) 0.93 (0.1) 1 (0) 0.97 (0.1) 1 (0)
S6PK 1 (0) 0.14 (0.01) 0.98 (0.08) 0.98 (0.06) 0.975 (0.08) 0.98 (0.06) 0.93 (0.24) 0.98 (0.06)
  1. The values in parentheses indicate the standard deviation for all 10 samples in a 10-fold cross validation.
  2. HMM-d – HMM profile used with default threshold
  3. HMM-t – HMM profile used with optimised threshold
  4. HMM-ModE – profile with modified emission probabilities
  5. HMM-Sub – Log-difference-of-odds-score method
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BMC Bioinformatics

ISSN: 1471-2105

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