Fig. 5
From: 3D deep convolutional neural networks for amino acid environment similarity analysis
Confusion matrices for predictions of the 20 amino acid microenvironments. Predictions on the training and test datasets using 3DCNN and FEATURE Softmax Classifier are summarized into confusion matrices to inspect the propensity of each microenvironment type to be predicted as one another. The 20 amino acids are arranged according to knowledge-based amino acid groups, where amino acids known to be biochemically similar are adjacent. The ith, jth element of the matrices shows the probability of examples of true label i being predicted as label j. The probability is represented in heat map colors. a 3DCNN-Train. b 3DCNN-Test. Local block structures in the confusion matrices for 3DCNN demonstrate similarities and differences between amino acid microenvironments. For example, phenylalanine (F), tryptophan (W), and tyrosine (Y) form a hydrophobic and aromatic block. Similar block structure is observed for test predictions. The captured features are robust across protein families. c FEATURE-Train. d FEATURE-Test. Block structures are less evident in the confusion matrices for the FEATURE Softmax classifier