Abstract: The present invention relates to a method for diagnosing cancer and predicting cancer type by using the terminal sequence frequency and the size of a cell-free nucleic acid fragment, and, more specifically, to a method for diagnosing cancer and predicting cancer type by using a method for extracting nucleic acids from a biospecimen so as to derive the terminal sequence frequency of a nucleic acid fragment and the size of the nucleic acid fragment on the basis of a read obtained by acquiring and aligning sequence information, generating vectorized data from same, and then inputting the data into a trained artificial intelligence model so as to analyze a calculated value.

Abstract: The present invention relates to a blood cell-free DNA-based method for predicting prognosis of breast cancer treatment and, more particularly, to a cell-free DNA-based method for predicting prognosis of breast cancer treatment, the method comprising a step of extracting cell-free DNA (cfDNA) from a biological sample before anticancer treatment, acquiring sequence information, then obtaining an I-score by using normalization correction and regression analysis of chromosomal regions, and analyzing the I-score and image information of the breast together after the anticancer treatment. A method for predicting prognosis of breast cancer, according to the present invention, uses next generation sequencing (NGS) so as to increase the accuracy of predicting the prognosis of a breast cancer patient and also increase the accuracy of prognosis prediction based on a very low concentration cell-free DNA of which detection has been difficult, thereby increasing the commercial utilization thereof.

Abstract: An artificial-intelligence-based cancer diagnosis and cancer type prediction method is described, which extracts nucleic acids from a biological sample to acquire sequence information, generates vectorized data on the basis of aligned nucleic acid fragments, and then inputs same into a trained artificial intelligence model to analyze a calculated value. Compared with a conventional method, which uses a step of determining the number of chromosomes on the basis of a read count and utilizes each related value as a normalized value, the artificial-intelligence-based cancer diagnosis and cancer type prediction method according to the present disclosure generates vectorized data to perform an analysis using an AI algorithm, and thus is useful in that similar effects can be exhibited even when read coverage is low.

Abstract: The present invention relates to an artificial-intelligence-based cancer diagnosis and cancer type prediction method, and, more particularly, to an artificial-intelligence-based cancer diagnosis and cancer type prediction method, which extracts nucleic acids from a biological sample to acquire sequence information, and thus generate vectorized data on the basis of aligned nucleic acid fragments, and then inputs same into a trained artificial intelligence model to analyze a calculated value. Compared with a conventional method, which uses a step of determining the number of chromosomes on the basis of a read count and utilizes each related value as a normalized value, the artificial-intelligence-based cancer diagnosis and cancer type prediction method according to the present invention generates vectorized data to perform an analysis using an AI algorithm, and thus is useful in that similar effects can be exhibited even when read coverage is low.

Abstract: The present invention relates to a method of diagnosing cancer and predicting cancer type using cell-free nucleic acid fragments and image analysis technology, and more particularly, to a method of diagnosing cancer and predicting cancer type by extracting nucleic acids from a biological sample to obtain sequence information (reads), aligning the obtained reads, generating an image including size and coverage information of nucleic acid fragments based on the aligned reads, and then analyzing values calculated by inputting the image into a trained artificial intelligence model. The method of diagnosing cancer and predicting cancer type using size and coverage information of cell-free nucleic acid fragments according to the present invention advantageously shows high sensitivity and accuracy because it generates vectorized data and performs analysis using an AI algorithm.

Abstract: The present invention relates to an artificial intelligence-based method for early diagnosis of cancer and, more specifically, to an artificial intelligence-based method for early diagnosis of cancer, using a method of inputting and analyzing information on cell-free DNA distribution in a tissue-specific regulatory region into an artificial intelligence model that has been trained to diagnose cancer early. The method for early diagnosis of cancer according to the present invention is high in commercial availability because it takes advantage of the information, obtained from the Next Generation Sequencing (NGS), on cell-free nucleic acid distribution in a tissue-specific regulatory region in early diagnosing cancer at high accuracy and sensitivity. Therefore, the method of the present invention is advantageous for early diagnosis of cancer.

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: Disclosed are a method for diagnosing cancer and predicting a cancer type using characteristics of cell-free nucleic acids. More preferably, disclosed are an artificial intelligence-based method for diagnosing cancer and predicting a cancer type using characteristics of cell-free nucleic acids, the method including extracting nucleic acids from a biological sample to obtain sequence information (reads), acquiring information associated with the distribution of cancer-specific single nucleotide variants (regional mutation density, RMD), the frequency of cancer-specific single nucleotide variants depending on types of mutations (mutation signature), the end sequence motif frequency of nucleic acid fragments, and the size of nucleic acid fragments based on the aligned reads, inputting the information to an artificial intelligence model, and analyzing integrated output values.

Abstract: Disclosed is a method of diagnosing cancer and predicting the type of cancer based on a single nucleotide variant in a cell-free nucleic acid including extracting nucleic acids from a biological sample to obtain sequence information, extracting cancer-specific single nucleotide variants through filtering based on aligned reads, calculating the regional mutation density of single nucleotide variants and the frequency of mutation signature of single nucleotide variants, and inputting the calculated values into a trained artificial intelligence model to analyze output values. This method is capable of exhibiting high sensitivity and accuracy compared to other methods of diagnosing cancer and predicting the type of cancer using genetic information of cell-free nucleic acids, and of ensuring the same level of sensitivity and accuracy as cancer-tissue-cell-based methods, and can be usefully applied to other analyses using single nucleotide variants in cell-free nucleic acids.

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: Disclosed is a method for diagnosing cancer and predicting a cancer type using fragment end motif frequencies and sizes of cell-free nucleic acid, and more preferably, to a method for diagnosing cancer and predicting a cancer type by extracting nucleic acids from a biological sample to obtain sequence information, acquiring fragment end motif frequencies and sizes of nucleic acids based on the aligned reads, converting the fragment end motif frequencies and sizes of nucleic acids into vectorized data, inputting the vectorized data to a trained artificial intelligence model and analyzing a resulting calculated value. The method includes generating vectorized data and analyzing the same using an AI algorithm and thus is useful due to high sensitivity and accuracy thereof even in the case of low read coverage.

Abstract: The present invention relates to an artificial-intelligence-based cancer diagnosis and cancer type prediction method, and, more particularly, to an artificial-intelligence-based cancer diagnosis and cancer type prediction method, which extracts nucleic acids from a biological sample to acquire sequence information, and thus generate vectorized data on the basis of aligned nucleic acid fragments, and then inputs same into a trained artificial intelligence model to analyze a calculated value. Compared with a conventional method, which uses a step of determining the number of chromosomes on the basis of a read count and utilizes each related value as a normalized value, the artificial-intelligence-based cancer diagnosis and cancer type prediction method according to the present invention generates vectorized data to perform an analysis using an AI algorithm, and thus is useful in that similar effects can be exhibited even when read coverage is low.

Abstract: The present invention relates to a method for detecting chromosomal abnormality by using information about the distance between nucleic acid fragments and, more particularly, to a method for detecting chromosomal abnormality by using a method, which extracts a nucleic acid from a biological sample so as to acquire sequence information, and then calculate the distance between nucleic acid fragment Representative Positions. A method for determining chromosomal abnormality, according to the present invention, uses a method, which analyzes and uses, unlike a method using a step of determining a chromosomal quantity on the basis of a conventional read count, the concept of the distance between aligned nucleic acid fragments and thus the conventional method has decreasing accuracy when the read count decreases.

Abstract: The present disclosure is a user interface providing method for multi-party schedule management including transmitting, by the processor, a schedule management interface in which individual schedules of a plurality of users are arranged to a user device through a communication interface connected thereto, receiving, by the processor, a schedule registration request related to a first user among the plurality of users obtained from the schedule management interface through the communication interface, from the user device, determining, by the processor, a second user who is available for schedule registration, among the plurality of users, according to a type of the scheduler registration request and registering, by the processor, a cooperative schedule including the first user and the determined second user in the schedule management interface output through the display of the user device.

Abstract: The present invention relates to an artificial intelligence-based chromosomal abnormality detection method, and more specifically, to an artificial intelligence-based chromosomal abnormality detection method using a method that involves: extracting nucleic acids from a biological sample to generate vectorized data on the basis of DNA fragments arranged by acquiring sequence information; and then comparing a reference value and a value calculated by inputting the vectorized data into a trained artificial intelligence model.

Abstract: The present invention relates to a blood cell-free DNA-based method for predicting the prognosis of liver cancer treatment. A method for predicting the prognosis of liver cancer, according to the present invention, uses next generation sequencing (NGS) so as to increase the accuracy of prognosis prediction of a liver cancer patient and also increase the accuracy of prognosis prediction based on a very low concentration cell-free DNA of which detection has been difficult, thereby increasing the commercial utilization thereof. Therefore, the method of the present invention is useful for determining the prognosis of a liver cancer patient.

Abstract: The present invention relates to a swimming cap and swimwear, in which after the wet swimming cap, swimsuit and swimming goggles are washed with clean water, all the swimwear pieces are hung to be dried and stored on one hanger, and specifically, the swimming cap is completely dried on the inside without being folded. The hanger for a swimming cap and swimwear comprises: a frame section 11 with a hook, an uneven section 13 to hang swimming goggles or swimsuits, and a cap receiving section 12 to hang a swimming cap.

Abstract: Disclosed are an apparatus for forecasting of hydrometeor classification using numerical weather prediction model and a method thereof. That is, a dual-polarized variables are generated using a numerical weather prediction model forecast field, the generated dual-polarized variables and a temperature of the numerical weather prediction model are interpolated, and then a hydrometeor is classified using fuzzy techniques to forecast information on the hydrometeors in the air in the future and forecast the information on the hydrometeors by a hydrometeor classification degree of an observation blank area.

Abstract: A steam cooking apparatus with improved water supply and drainage structures. The apparatus includes a body, a cooking compartment, a steam generator to supply steam into the cooking compartment, a water vessel to store water and supply the water into the steam generator, a water supply device including a holder and a slider slidably mounted in the holder so as to be withdrawn from the body, a first water supply tube connecting the slider and the water vessel, a second water supply tube connecting the water vessel and the steam generator, and a drain tube to drain water in the steam generator to the outside of the body. The drain tube includes an end fixed to the slider so that the end of the drain tube is withdrawn with the slider from the body when water in the steam generator is drained to the outside.