Abstract: A method for classifying individuals in mixtures of DNA is disclosed. The method comprises: Provide next-generation sequencing (NGS) data which comprises raw sequence reads originated from mixtures of DNA; performing a data processing procedure to generate a plurality of sparse matrix; and input the plurality of sparse matrix into a trained deep learning model installed on computers to classify individuals in the mixtures of DNA. In particular, the method is used to classify individuals in mixture of the DNAs from forensic dataset or whole exome sequencing dataset.

Abstract: The present invention provides a method for identifying radiation induced genes and long non-coding RNAs and its application thereof, the method comprises the steps of: (1). Provide expression values of genes and long non-coding RNAs; (2). Execute weighted gene correlation network analysis (WGCNA) by a computer system to calculate Pearson correlation coefficients of pairs of the genes and long non-coding RNAs based on the expression values of the genes and long non-coding RNAs; and (3). Perform a screening step by the computer system to identify radiation induced genes and long non-coding RNAs based on the Pearson correlation coefficients of the pairs of the genes and long non-coding RNAs with a value more than 0.75.

Abstract: A drug combination prediction method comprising: storing a plurality of original gene sets, at least one first gene impacted by a first drug and at least one second gene impacted by a second drug; determining the part of the at least one first gene and the part of the at least one second gene to be a first interaction gene set; calculating a gene amount of the first interaction gene set to obtain a first interaction gene amount, and calculating a first percentage generated by the first interaction gene amount in the first original gene set; calculating an interaction value of the combination of the first drug and the second drug according to the first percentage; and selecting at least one synergistic pharmaceutical composition according to the interaction value.

Abstract: The invention is directed to a method to predict prognostic results for diseases including acute myeloid leukemia (AML) by analyzing novel markers which comprises microRNA/mRNA (miRNA/mRNA) pairings. In particular, the miRNA/mRNA pairings are a kind of NPM1 mutation-modulated miRNA/mRNA regulation pairs.

Abstract: A system for analyzing sequencing data of bacterial strains and a method thereof are provided. The method for analyzing sequencing data of bacterial strains includes the following steps: searching a specific variable region of a first genetic sample sequence and searching another specific variable region of a second genetic sample sequence; determining whether both the specific variable region and the another specific variable region have an identical cross-sample subsequence; if both the specific variable region and the another specific variable region have the identical cross-sample subsequence, storing the cross-sample subsequence into a recording table; and if the identical cross-sample subsequence exists, comparing the cross-sample subsequence with a plurality of gene sequences of known strains stored in a database module to analyze a plurality of strains corresponding to the cross-sample subsequence in the first genetic sample sequence and the second genetic sample sequence.

Abstract: The present invention relates to novel genetic markers associated with response of a patient with esophageal cancer (ECa) to chemoradiation therapy, and particularly to methods and kits for predicting an ECa patient's response to chemoradiation therapy by genotyping of the markers.

Abstract: The present invention relates to novel genetic markers associated with response of a patient with esophageal cancer (ECa) to chemoradiation therapy, and particularly to methods and kits for predicting an ECa patient's response to chemoradiation therapy by genotyping of the markers.

Abstract: The present invention relates to novel genetic markers associated with response of a patient with esophageal cancer (ECa) to chemoradiation therapy, and particularly to methods and kits for predicting an ECa patient's response to chemoradiation therapy by genotyping of the markers.