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Table 2 Comparison of Nepal with other alignment methods

From: Derivative-free neural network for optimizing the scoring functions associated with dynamic programming of pairwise-profile alignment

 

Remote

[0,20)a

(1405 files)

Medium

[20,40)a

(1790 files)

All

[0,40)a

(3195 files)

Sensitivity

 Nepal

0.5317

0.8343

0.7012

 Cosine

0.5045**

0.8246**

0.6838**

 CC

0.5135**

0.8269**

0.6891**

 MIQS

0.2775**

0.7316**

0.5319**

 BL62

0.2333**

0.6955**

0.4923**

Precision

 Nepal

0.5031

0.8102

0.6751

 Cosine

0.4753**

0.7999**

0.6571**

 CC

0.4858**

0.8032**

0.6636**

 MIQS

0.2654**

0.7134**

0.5164**

 BL62

0.2317**

0.6902**

0.4885**

  1. Cosine, CC, MIQS, and BL62, indicate profile comparison methods with cosine similarity and correlation coefficient and sequence comparison methods with MIQS and BLOSUM62
  2. ** P < 0.01, Wilcoxon signed rank test with Bonferroni correction
  3. aSequence identity (%) of each division