Abstract: A method for evaluating a risk of a subject getting a specific disease includes steps of: storing a reference database that contains original parameter sets; selecting target alleles from an SNP profile derived from genome sequencing data of a subject; selecting target parameter sets from among the original parameter sets; calculating, for each of the target parameter sets, a race factor based on a global risk allele frequency and a group-specific risk allele frequency included in the target parameter set; calculating a genetic factor based on statistics, global reference allele frequencies, the race factors for the target parameter sets, and numbers of chromosomes in homologous chromosome pairs included in the target parameter sets; calculating a citation factor based on numbers of citation times included in the target parameter sets; and calculating a risk score based on the genetic factor and the citation factor.

Abstract: A method includes steps of: based on protein structure data, selecting a residue pair that includes a specific residue and a paired residue respectively of two wild-type protein chains of a protein complex; determining a mutant residue to substitute for the specific residue; for a target interface between the mutant residue and the paired residue, calculating an atomic distance and an atomic interaction force based on the protein structure data and amino acid structure data; and estimating binding free energy of the target interface by feeding the atomic distance, the atomic interaction force, and physicochemical information related to the specific residue and the mutant residue into a deep neural network.

Abstract: A method includes: determining a mutation frequency for each residue in a wild-type spike protein of a specific virus; for each residue in the protein, counting a total number of contact residues related to said each residue and P antibodies based on P entries of protein structure data; for each residue in the protein, for a condition that said each residue mutates into one common amino acid residue, determining a normalized binding free energy value using a pre-established model based on the protein structure data, and determining a mutation effect score based on the mutation frequency, the total number of contact residues and the normalized binding free energy value; generating a mutation effect epitope map related to the mutation effect scores determined for all residues in the protein and all common amino acid residues; and determining, based on the map, a region in the protein as a target epitope.