Abstract: The present invention relates to a method for early diagnosis of cancer, using artificial-intelligence-based detection of a tumor-derived mutation of cell-free DNA and, more specifically, to a method for early diagnosis of cancer, using artificial-intelligence-based detection of a tumor-derived mutation of cell-free DNA, the method using a method comprising obtaining sequence information from a biological sample, and then comparing the sequence information with that of a reference genome to detect a mutation, and inputting the detected mutation information into an artificial intelligence model trained to determine the presence of a tumor-derived mutation and analyzing same.

Abstract: This method of predicting the T cell activation of peptide-MHC comprises the steps in which an analysis apparatus: receives genetic data of a patient; identifies, on the basis of the genetic data, a first amino acid sequence of a major histocompatibility complex (MHC) and a second amino acid sequence of antigen generated by tumor cells; produces a matrix indicating the interrelationship between the first amino acid sequence and the second amino acid sequence in a single amino acid unit; and inputs the matrix to a trained neural network model to determine whether the T cells secrete at least a threshold amount of cytokine as a result of the binding of the MHC and the antigen.

Abstract: A method for discovering a target antigen for a chimeric antigen receptor comprises the steps of: obtaining, by an analysis apparatus, expression information of genes of tumor cells in single cell units; extracting, by the analysis apparatus, from among the genes, genes of candidate antigens having an expression level of a reference value or more; determining, by the analysis apparatus, all possible gene combinations that can be constituted by the genes of the candidate antigens; and determining, by the analysis apparatus, a target gene combination in which at least one gene included therein is expressed, among the gene combinations.

Abstract: The present invention relates to a method for determining sensitivity to a PARP inhibitor or a DNA damaging agent by using a non-functional transcriptome, and more particularly, to a method for determining sensitivity to a PARP inhibitor or a DNA damaging agent by extracting nucleic acids from a biological sample to obtain the expression amount of each of non-functional transcriptomes of DNA repair-related genes, and then analyzing the use rate (TU) of each of the non-functional transcriptomes for each gene on the basis of the obtained expression amount. The method for determining sensitivity to a PARP inhibitor or a DNA damaging agent according to the present invention is useful in that sensitivity can be determined in real time with high accuracy because the method uses information of the transcriptomes transcribed in the gene, unlike existing methods which determine sensitivity to a PARP inhibitor or a DNA damaging agent on the basis of genetic variation information.

Abstract: Provided are a method and system for screening neoantigen and uses of neoantigens. Specifically, provided are a method and system for screening neoantigens derived from a gene of which expression is essential for survival of a cancer cell and/or a is homogeneously expressed in all cells in cancer tissue as a diagnostic and/or therapeutic target, and uses of neoantigens.

Abstract: A machine learning model-based essential gene identification method includes receiving, by an analysis apparatus, inputs of expression pattern information on genes of a specific cell; inputting, by the analysis apparatus, the expression pattern information to a machine learning model; and determining, by the analysis apparatus, whether a target gene from among the genes is essential in the survival of the cell on the basis of information output by the machine learning model.

Abstract: Disclosed is a method for providing information for predicting a response to cancer treatment, the method including: obtaining information on a global DNA methylation level detected from a sample of a cancer patient; and evaluating a response to cancer treatment based on the information on the global DNA methylation level.