Methods for diagnosing cancer and predicting cancer type using end sequence motif frequency and size of cell free nucleic acid fragments

The present invention relates to a method for diagnosing cancer and predicting the type of cancer by using the motif frequency and size of the terminal sequence of a cell-free nucleic acid fragment, and more specifically, to a method for diagnosing cancer and predicting the type of cancer by extracting nucleic acid from a biological sample on the basis of a read obtained by obtaining and aligning sequence information. A method of diagnosing cancer and predicting the type of cancer by deriving the end sequence motif frequency of a nucleic acid fragment and the size of the nucleic acid fragment, generating vectorized data therefrom, inputting the data into a trained artificial intelligence model, and analyzing the calculated values. Since, according to the present invention, the method for diagnosing cancer and predicting cancer type by using the end sequence motif frequency and size of the cell free nucleic acid fragment generates vectorized data and analyzes the data by using the AI algorithm, the method show