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. 2021 Feb 1;24(3):102131. doi: 10.1016/j.isci.2021.102131

Figure 7.

Figure 7

Distribution of different memory types across diverse biological systems

(A and B) The memory capacity of GRNs can be systematically classified according to their features. (A) A classification of GRNs based on whether they correspond to vertebrate or invertebrate species. This panel shows that vertebrate GRNs tend to contain more memory than the invertebrates, as quantified by the classification performance metrics: Accuracy = 0.74, Sensitivity = 0.88, Specificity = 0.63, Positive predictive value = 0.67, Negative predictive value = 0.86, and AUC = 0.75. Red borders indicate data from invertebrate GRNs, whereas green borders indicate data from vertebrate GRNs. (B) A classification of GRNs based on whether they derived from a unicellular or generic process or from a specific somatic cell type. This panel shows that the GRNs corresponding to the non-generic cell types tend to contain more memory than the generic ones, as quantified by the classification performance metrics: Accuracy = 0.86, Sensitivity = 0.88, Specificity = 0.84, Positive predictive value = 0.82, Negative predictive value = 0.89, and AUC = 0.86. Classification was performed as follows. First, the memory capacity of each GRN was computed as the proportion of memory within the total that included the “no-memory” type. Then, if the memory capacity of a GRN exceeded 50% it was categorized under the “memory” class or in the “no memory” class otherwise. The standard binary classification metrics reported above were computed based on the associated confusion matrix containing the number of True-Positives (TP), False-Positives (FP), True-Negatives (TN), and False-Negatives (FN) where the “memory” class is the “positive” class and the “no-memory” class is the “negative” class. As per standard definitions, Accuracy is the proportion of TP and TN among the total number of instances, Sensitivity is the proportion of TP among the actual positive instances, Specificity is the proportion of TN among the actual negative instances, Positive predictive value is the proportion of TP among the predicted positive instances, Negative predictive value is the proportion of TN among the predicted negative instances, and AUC is the area under the receiver operating characteristic curve, which can be interpreted as the probability that the classifier will rank a randomly chosen positive instance higher than a randomly chosen negative instance.