This feature, which stands for auto covariance transformation, for jth column calculates the average of this column, and then subtracts the resulting number from the elements on the i and (i + g)th rows of this column, and finally multiplies them. by changing the variable i from 1 to L-g, it calculates the sum of these, since the variable j changes between 1 and 20, and the variable g between 1 and 10 eventually a feature vector of length 200 will be obtained.
name of PSSM Matrix files
feature vector of length 200
L. Zou, C. Nan, and F. J. B. Hu, "Accurate prediction of bacterial type IV secreted effectors using amino acid composition and PSSM profiles," vol. 29, no. 24, pp. 3135-3142, 2013.
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