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Table 3 Leave-one-species-out results using SVM and Random Forest classifiers

From: Sequence-based information-theoretic features for gene essentiality prediction

  Our method Liu et al. Palaniappan and Mukherjee Geptop (homology) Geptop* (Composition)
Training on (No. of species) 14 30 14 18 18
  Random Forest SVM SVM SVM Score based Score based
AB 0.81 0.83 0.75 0.74 0.85 0.79
BS 0.84 0.84 0.77 0.58 0.95 0.81
EC 0.87 0.88 0.83 0.65 0.95 0.84
FN 0.83 0.83 0.67 0.66 0.84 0.74
HI 0.75 0.77 0.54 0.46 0.57 0.59
HP 0.75 0.74 0.52 0.59 0.60 0.64
MG 0.68 0.66 0.60 0.64 0.72 0.56
MP 0.75 0.74 0.64 0.61 0.87 0.76
MT 0.80 0.77 0.70 0.49 0.73 0.77
PA 0.80 0.80 0.65 0.66 0.80 0.79
SA 0.88 0.90 0.81 0.66 0.84 0.86
SA2 0.86 0.85 0.80 - 0.88 0.83
SE 0.86 0.86 0.69 - 0.95 0.86
ST 0.81 0.79 0.84 0.53 0.71 0.69
VC 0.75 0.72 0.69 - 0.61 0.72
Average 0.80 0.80 0.70 0.61 0.79 0.75
  1. The average AUC scores of four existing methods are also presented for comparison. Geptop* is a sequence composition based predictor presented along with Geptop [23]