Abstract: A method of detecting biomarkers using an artificial intelligence (AI) deep learning model for conversion data of nucleotide sequences and mutations of population genomes, the method including: collecting nucleotide sequences and mutations of population genomes; generating conversion data by reflecting mutations of diploid genomes in the collected nucleotide sequences; performing an artificial intelligence (AI) deep learning model with the generated conversion data; generating a fully connected network (FCN) by connecting the results obtained by the machine learning; and extracting biomarkers by the learned model.

Abstract: A semiconductor device includes a semiconductor substrate, an integrated device ort the semiconductor substrate, a first redistribution layer on the semiconductor substrate, the first redistribution layer having first conductive patterns electrically connected to the integrated device, a second redistribution layer on the first redistribution layer, the second redistribution layer having second conductive patterns connected to the first conductive patterns, and third conductive patterns on a top surface of the second redistribution layer. The third conductive patterns include pads connected to the second conductive patterns, under-bump pads spaced apart from the pads, a grouping pattern between the pads and an outer edge of the second redistribution layer, and wiring lines that connect the under-bump pads to the pads and connect the pads to the grouping pattern.

Abstract: Disclosed are a system and a method of predicting neoantigens and immune response induction. The system and the method may verify induction of immunity against neoantigens having high binding affinity by identifying neoantigen candidates through genomic mutations and then predicting the binding affinities of the neoantigen candidates for MHC through molecular dynamics. The method provides neoantigen immunotherapy information for identifying a neoantigen using artificial intelligence (AI)-based molecular dynamics big data, and includes steps of: (A) identifying neoantigen candidates through a genomic mutation; (B) filtering the specificities of the neoantigen candidates for tissue and disease; (C) predicting the in silico binding of the neoantigens to MHC; and (D) calculating and ranking TCR activity.

Abstract: A semiconductor device includes a semiconductor Substrate, an integrated device ort the semiconductor substrate, a first redistribution layer on the semiconductor substrate, the first redistribution layer having first conductive patterns electrically connected to the integrated device, a second redistribution layer on the first redistribution layer, the second redistribution layer having second conductive patterns connected to the first conductive patterns, and third conductive patterns on a top surface of the second redistribution layer. The third conductive patterns include pads connected to the second conductive patterns, under-bump pads spaced apart from the pads, a grouping pattern between the pads and an outer edge of the second redistribution layer, and wiring lines that connect the under-bump pads to the pads and connect the pads to the grouping pattern.

Abstract: An image of a receipt may be received by a service provider from a user device of a user. The receipt may be a record of a transaction between the user and a physical merchant. The service provider may extract text from the image and apply a predictive model trained on text data of historical receipts to the extracted text to identify keywords corresponding to items purchased in the transaction. The service provider may generate a shopping list for the user and/or may compare the keywords to descriptions of catalog items in an online catalog maintained by the service provider to identify catalog items that are the same or similar to the purchased items. The service provider may associate the catalog items with the user's account maintained by the service provider and generate a user interface to present on the user device that includes an indication of the catalog items.

Abstract: A semiconductor device includes first power supply lines arranged in a first direction and extended in a second direction, second power supply lines arranged in the second direction and extended in the first direction, power gating switches, and taps, Each of the power gating switches is connected with one of the first power supply lines and at least two of the second power supply lines. Each of the taps is connected with one of the first power supply lines or one of the second power supply lines. One of the power gating switches closest to a first power gating switch is a second power gating switch, one of the taps closest to the first power gating switch is a first tap, and at least one of the second power gating switch and the first tap is spaced from the first power gating switch in a third direction.

Abstract: Disclosed is a human haplotyping method. The method includes: collecting a sequence of a gene to be analyzed; matching and aligning reads of the collected sequence to a reference stored in a database; electing candidate alleles from among alleles of the reference; and selecting a final allele from among the candidate alleles.

Abstract: A method of detecting biomarkers using an artificial intelligence (AI) deep learning model for conversion data of nucleotide sequences and mutations of population genomes, the method including: collecting nucleotide sequences and mutations of population genomes; generating conversion data by reflecting mutations of diploid genomes in the collected nucleotide sequences; performing an artificial intelligence (AI) deep learning model with the generated conversion data; generating a fully connected network (FCN) by connecting the results obtained by the machine learning; and extracting biomarkers by the learned model.

Abstract: A system of predicting drug indications and drug response using an artificial intelligence (AI) deep learning model based on convergence of different types of information, the system including: a learning module configured to learn the response correlation between structure information on a drug and genetic information on a genome from collected learning information by deep machine learning; a prediction module configured to receive analysis information and output the result of prediction of the response of the genome to the drug from the analysis information; and a storage module configured to store a response prediction algorithm learned by the learning module. The learning information is drug response information obtained from clinical drug response information on target proteins, cell lines or living bodies.

Abstract: Disclosed is a human haplotyping method. The method includes: collecting a sequence of a gene to be analyzed; matching and aligning reads of the collected sequence to a reference stored in a database; electing candidate alleles from among alleles of the reference; and selecting a final allele from among the candidate alleles.

Abstract: Disclosed is a system for genotype analysis using genetic variation information on a personal genome. The system includes an analysis data input unit configured to receive analysis data including personal genomic information; a search control unit configured to produce analysis results including a genotype of each gene or genotype versus phenotype by comparing genetic information stored in a database with the analysis data and to generate a result report based on the analysis results; and a storage unit comprising a haplotype DB that stores genotype information on genes of a control group to compare with the analysis data. The search control unit includes a HaploScan engine configured to determine the genotype of the analysis date by comparing the analysis data with the haplotype DB.

Abstract: A system and method for transmitting documents from a document device, such as an MFP. The MFP accesses calendar and workflow servers to determine a list of information items. A user can select an information item for which the document is to be associated and the document device will then determine all of the associated document destinations, e.g., workspaces, email addresses, and the like, and then subsequently transmit the documents to all of the document destinations that are associated with the information item or a subset of the document destination associated with the information item as selected by the user.

Abstract: A system and method for transmitting documents from a document device, such as an MFP. The MFP accesses calendar and workflow servers to determine a list of information items. A user can select an information item for which the document is to be associated and the document device will then determine all of the associated document destinations, e.g., workspaces, email addresses, and the like, and then subsequently transmit the documents to all of the document destinations that are associated with the information item or a subset of the document destination associated with the information item as selected by the user.