Figure 1: RBF network representation of the relations between individual mtSNPs and eight classes of people. The input layer is the set of mtSNP sequences represented numerically (A, G, C, and T are converted to 1, 2, 3, and 4). The hidden layer classifies the input vectors into several clusters depending on the similarities of individual input vectors. The output layer is determined depending on which analysis is carried out. In the case of centenarians, 1 corresponds to centenarians and 0 corresponds to other seven classes of people. In the case of AD patients, 1 corresponds to AD patients and 0 corresponds to other seven classes of people. The other classes of people (semi-supercentenarians, PD patients, T2D patients, healthy non-obese young males, and healthy obese young males) are carried out in a similar way. Xi : i-th input vector, TN : maximum number of vectors (in this example, TN=448 (64x7)), TSNP : maximum number of mtSNPs (in this example, TSNP =562), Mm: the location vector, m: the number of basis functions, μ: basis function, σ: standard deviation, wi: i-th weighting variable, f(X): weighted sum function.