1. A method by computer of determining whether or not one of multiple known feature patterns that are similar to each other or a new feature pattern that is similar to the known feature pattern is included in a biological sequence represented by a finite number of discrete symbols, comprising the steps of: providing a sample of a biological material; analyzing the biological material to determine a biological sequence contained therein; inputting the biological sequence into a computer; assigning a discrete symbol for each component making up the biological sequence; providing the multiple types of the known feature patterns; providing multiple types of non-feature patterns that are different from the known feature patterns; assigning a discrete symbol for each component making up the biological sequences of the multiple known feature patterns and multiple non-feature patterns; generating a symbol frequency for each of the discrete symbols at each of the positions in the feature patterns using the multiple types of known feature patterns; generating a symbol-frequency table by corresponding the symbol frequencies to the positions and types of the discrete symbols in the feature patterns; converting to numerals the multiple types of known feature patterns and the multiple types of non-feature patterns that are different from the known feature patterns according to the positions and types of the discrete symbols by using the symbol-frequency table; binding the numerals to generate a training data matrix and performing an independent component analysis or a principal component analysis by using the training data matrix to generate a separation matrix, as a matrix for performing inverse transformation of a basis matrix including feature elements representing the feature patterns; generating test data by converting to numerals the biological sequence according to the positions and types of discrete symbols by using the symbol-frequency table; obtaining separation data or a separation data matrix in which a plurality of the separation data are bound by performing a matrix calculation of multiplying the separation matrix with the test data or a test data matrix in which a plurality of the test data are bound; and deciding whether or not one of the multiple types of the known feature patterns or the new feature pattern similar to the known feature patterns is included in the biological sequence or deciding a degree of existence thereof by using values of feature decision elements assigned according to internal-matrix positions of the feature elements included in the basis matrix among elements constituting the separation data or the separation data matrix.