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Table 6 Detailed results achieved by the proposed MLTrigNer Model, Basic Model A and TL Model C on DataMLEE

From: Multiple-level biomedical event trigger recognition with transfer learning

Trigger type Basic Model A TL Model C MLTrigNer Model
  P R F1 P R F1 P R F1
Cell proliferation 85.37 81.40 83.33 83.33 81.40 82.35 81.40 81.40 81.40
Development 66.37 76.53 71.09 74.51 77.55 76.00 78.35 77.55 77.95
Blood vessel develop 97.33 94.19 95.74 98.64 93.87 96.20 98.99 94.84 96.87
Growth 96.00 85.71 90.57 88.89 85.71 87.27 92.45 87.50 89.91
Death 73.68 75.68 74.67 66.67 81.08 73.17 66.67 81.08 73.17
Breakdown 82.35 63.64 71.79 73.68 63.64 68.29 87.50 63.64 73.68
Remodeling 71.43 50.00 58.82 75.00 30.00 42.86 66.67 40.00 50.00
Synthesis 50.00 25.00 33.33 33.33 25.00 28.57 20.00 25.00 22.22
Gene expression 91.67 83.33 87.30 85.51 89.39 87.41 89.05 92.42 90.71
Transcription 0.0 0.0 0.0 50.00 16.67 25.00 100.0 16.67 28.57
Protein Catabolism 0.0 0.0 0.0 0.0 0.0 0.0 33.33 20.00 25.00
Phosphorylation 75.00 100.0 85.71 100.0 100.0 100.0 100.0 100.0 100.0
Dephosphorylation 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
Localization 78.83 81.20 80.00 77.14 81.20 79.12 82.17 79.70 80.92
Binding 86.96 70.18 77.67 83.02 77.19 80.00 79.25 73.68 76.36
Regulation 59.80 58.93 59.37 65.13 61.65 63.34 65.17 63.29 64.22
Positive regulation 80.88 81.90 81.39 81.11 82.91 82.00 81.96 82.22 82.09
Negative regulation 84.73 65.71 74.02 77.18 75.61 76.39 80.72 73.47 76.92
Planned process 78.69 48.98 60.38 66.86 57.65 61.92 71.07 57.65 63.66
TOTAL 81.63 74.26 77.77 79.69 77.62 78.64 81.76 77.71 79.68
  1. The Basic Model A is trained only on the training and development sets of DataMLEE without transfer learning. The TL Model C and the MLTrigNer model are jointly trained on the source dataset DataEPI11 and the training and development sets of the target dataset DataMLEE using different transfer learning approaches, respectively. The three models are tested on the test set of DataMLEE. In the results of MLTrigNer Model, the improved F1 values are marked in bold