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Table 2 Experimental results based on VLDB GRU algorithm

From: Research on RNA secondary structure predicting via bidirectional recurrent neural network

Dataset Method SEN PPV ACC MCC
SPR GRU 0.777 0.687 0.707 0.421
FLAG 0.965 0.853 0.905 0.816
VLDB 0.962 0.885 0.921 0.845
ASE GRU 0.983 0.482 0.556 0.323
FLAG 0.806 0.532 0.645 0.36
VLDB 0.826 0.652 0.727 0.475
RFA GRU 0.971 0.563 0.661 0.451
FLAG 0.793 0.613 0.732 0.473
VLDB 0.811 0.699 0.778 0.558
SRP GRU 0.768 0.676 0.708 0.421
FLAG 0.798 0.653 0.697 0.408
VLDB 0.828 0.828 0.729 0.463
TMR GRU 0.974 0.498 0.63 0.434
FLAG 0.819 0.668 0.769 0.543
VLDB 0.796 0.669 0.765 0.529
  1. The data in bold, italics, underline represent the optimal evaluation index values obtained by different algorithms on the same data set