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Table 2 Correlation results on Sample A, replicate 1

From: Sequence-specific bias correction for RNA-seq data using recurrent neural networks

Method Pearson Spearman r 2
Raw normalized counts 0.8644 0.8708 0.7471
seqBias 0.8658 0.8751 0.7496
GRU(4-10-4) 0.8674 0.8749 0.7523
LSTM(4-10-4) 0.8661 0.8736 0.7460
GRU(4-20-4) 0.8661 0.8729 0.7500
LSTM(4-20-4) 0.8669 0.8744 0.7515
  1. In total, 922 genes are evaluated. RNN(a-b-c) represents an RNN model with model structure defined according to a, b, and c. The values a and c represent the node number of the input and output layer, respectively. The value b is the number of hidden units. The boldface numbers indicate the best performance in all comparisons