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Table 2 The top 25 most informative features.

From: MiRTif: a support vector machine-based microRNA target interaction filter

Feature μ + σ + M - σ - F
3-gram, non-seed, mismatch/AU/AU 0.0197 0.0370 0.0000 0.0000 0.5313
2-gram, non-seed, mismatch/AU 0.0526 0.0606 0.0107 0.0353 0.4374
2-gram, entire, mismatch/AU 0.0441 0.0439 0.0160 0.0265 0.3991
3-gram, entire, GC/gap/gap 0.0068 0.0176 0.0228 0.0236 0.3904
3-gram, entire, mismatch/mismatch/gap 0.0060 0.0164 0.0000 0.0000 0.3636
3-gram, non-seed, gap/GU/AU 0.0095 0.0262 0.0000 0.0000 0.3631
3-gram, entire, gap/GU/AU 0.0062 0.0172 0.0000 0.0000 0.3629
3-gram, entire, mismatch/AU/AU 0.0198 0.0312 0.0044 0.0132 0.3457
3-gram, non-seed, mismatch/mismatch/AU 0.0212 0.0422 0.0022 0.0135 0.3417
2-gram, seed, GU/GC 0.0117 0.0352 0.0000 0.0000 0.3337
3-gram, entire, AU/mismatch/GU 0.0054 0.0167 0.0000 0.0000 0.3253
2-gram, entire, gap/gap 0.0838 0.1059 0.1512 0.1030 0.3226
1-gram, entire, GC 0.2399 0.0957 0.2893 0.0678 0.3021
1-gram, non-seed, gap 0.1880 0.1581 0.2841 0.1601 0.3020
2-gram, non-seed, gap/gap 0.1022 0.1406 0.1886 0.1505 0.2969
3-gram, non-seed, GC/mismatch/AU 0.0067 0.0224 0.0000 0.0000 0.2969
1-gram, entire, gap 0.1595 0.1225 0.2273 0.1066 0.2958
3-gram, non-seed, mismatch/mismatch/gap 0.0067 0.0227 0.0000 0.0000 0.2943
3-gram, entire, mismatch/mismatch/AU 0.0135 0.0259 0.0026 0.0111 0.2937
2-gram, entire, GC/gap 0.0199 0.0298 0.0357 0.0243 0.2932
2-gram, entire, GC/GC 0.0630 0.0549 0.0928 0.0471 0.2930
3-gram, entire, GU/GC/gap 0.0002 0.0028 0.0043 0.0115 0.2895
1-gram, non-seed, GC 0.1742 0.1261 0.2377 0.0952 0.2870
3-gram, entire, gap/GU/GC 0.0005 0.0047 0.0056 0.0136 0.2810
3-gram, non-seed, GU/mismatch/AU 0.0064 0.0233 0.0000 0.0000 0.2756
  1. Features are in the format of k- gram type, region, and k- gram code. For example, "3-gram, non-seed, mismatch/AU/AU" represent a mismatch followed by an AU pair followed by an AU pair in the non-seed region (see Materials and Method – Data representation for the detailed definitions of k- gram, region and k-gram code). For each feature, its means and standard deviations in both positive and negative sets are listed. The F score is defined as |(μ+ - μ-)/(σ+ + σ-)|, which measures the discriminating ability of the feature.