Abstract: The present disclosure relates to a method for providing information for predicting a response to cancer therapy, comprising the step of obtaining information on a DNA methylation level of a long interspersed nuclear element-1 (LINE-1) subfamily from an isolated sample of a cancer patient, wherein the LINE-1 subfamily is at least one selected from the group consisting of LIPA12_5end, LIHS_5end, L1PA10_3end, LIP2_5end, L1PA11_3end, L1P2_5end, L1PA13_3end, L1PA11_3end, and LIP1_orf2.

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: 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.