Fig. 6

Hierarchical Clustering of normalized confusion matrices. The ith, jth element of the row-normalized matrices shows the probability of examples of true label i being predicted as label j. The probability is represented in heat map colors. Hierarchical clustering reveals similarities between amino acid microenvironments in terms of their propensities to be assigned to the 20 amino acid types. a 3DCNN-Training. Amino acid groupings discovered by our 3DCNN generally agree with known amino acid similarities. Six clusters were discovered by our network. The first cluster includes phenylalanine, tryptophan, and tyrosine. These are the three amino acids known to be hydrophobic and aromatic. The second and third clusters comprises valine, isoleucine and leucine, methionine respectively, which are all non-polar and aliphatic. The polar amino acids form the fourth cluster. Amino acids with known distinct properties, glycine and cysteine do not form local blocks with the other amino acids. b 3DCNN-Test Groupings generated for the test examples are consistent with the training counterparts. c FEATURE-Softmax-Training. d FEATURE-Softmax-Test Clustering on the FEATURE Softmax classifier generates much coarser amino acid groupings than the ones discovered by 3DCNN. The two major groups, hydrophobic and polar amino acids are separated