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Table 6 GenSET 1 T3SS effector prediction on four organisms were compared to three other available machine learning programs. GenSET 1 performed better than other programs in three out of the four organisms tested except for S. dysenteriae (see Additional files 3 and 4 for the actual data)

From: Computational approach to predict species-specific type III secretion system (T3SS) effectors using single and multiple genomes

  Top 40 positive prediction out of total confirmed effectors
Programa S. dysenteriae E. coli P. syringae S. Typhimurium
EffectiveT3 7/24 (29.2%) 11/21 (52.4%) 9/51 (17.7%) 2/24 (8.3%)d
1/8 (12.5%)e
T3MM 15/24 (62.5%) c 5/21 (23.8%) 21/51 (41.2%) 4/24 (16.7%)d
5/8 (62.5%)e
BPBAac 13/24 (54.2%) 12/21 (57.1%) 20/51 (39.2%) 7/24 (29.2%)d
6/8 (75.0%)e
GenSET 1b 5/9 (55.6%) 5/6 (83.3%) 16/30 (53.3%) 8/9 (88.9%) d
4/8 (50%)e
  1. aOther programs namely SIEVE, T3SEpre, and Meta-analytic were not available or accessible at the time of investigation
  2. bFor the GenSET 1 method, 15 or 21 effectors were taken out from the total effectors as the positive data sets. Thus, the totals were less in numbers when compared to others
  3. cBold number denotes the highest value in a given column
  4. dTop 40 positive prediction for SPI-2 effectors
  5. eTop 40 positive prediction for SPI-1 effectors