Skip to main content

Table 1 Cross-validation performance comparison of different MultiLoc architectures trained using the BaCelLo and the Höglund datasets

From: MultiLoc2: integrating phylogeny and Gene Ontology terms improves subcellular protein localization prediction

Dataset Method Animals Fungi Plants
   AVG ACC AVG ACC AVG ACC
BaCelLo        
  MultiLoc 77.3 (± 2.9) 75.7 (± 3.1) 78.4 (± 2.7) 71.0 (± 2.6) 71.4 (± 6.8) 67.8 (± 3.8)
  + PhyloLoc 80.1 (± 2.4) 78.2 (± 2.9) 80.0 (± 2.5) 73.6 (± 0.9) 78.6 (± 3.6) 77.4 (± 1.9)
  + GOLoc 84.0 (± 1.7) 82.8 (± 2.0) 81.1 (± 0.5) 75.5 (± 1.1) 80.9 (± 4.4) 77.6 (± 3.5)
  MultiLoc2-LowRes 86.1 (± 1.4) 84.0 (± 1.7) 82.8 (± 2.2) 77.9 (± 0.5) 81.9 (± 4.1) 80.2 (± 3.5)
Höglund        
  MultiLoc 78.6 (± 1.2) 76.4 (± 1.2) 78.0 (± 1.3) 76.6 (± 1.2) 78.0 (± 1.8) 76.4 (± 1.7)
  + PhyloLoc 84.6 (± 0.7) 84.0 (± 0.6) 84.7 (± 1.4) 84.4 (± 0.9) 86.5 (± 1.5) 84.3 (± 0.7)
  + GOLoc 87.3 (± 1.8) 86.7 (± 1.0) 87.1 (± 0.9) 86.9 (± 0.8) 86.9 (± 1.4) 86.3 (± 1.1)
  MultiLoc2-HighRes 89.3 (± 1.4) 88.6 (± 1.0) 89.2 (± 1.1) 88.9 (± 1.2) 89.4 (± 0.8) 88.7 (± 0.9)
  1. This table compares the average sensitivities (AVGs) and overall accuracies (ACCs) of MultiLoc2-LowRes and MultiLoc2-HighRes with those of the original MultiLoc and the extended architecture based on PhyloLoc as well as GOLoc only. The AVGs and ACCs are given in percent. The standard deviations (in parentheses) refer to the differences of the AVGs and ACCs of the different cross-validation models.