Abstract: The present invention relates to a method for diagnosing cancer and predicting cancer types using a methylated cell-free nucleic acid, and more particularly, to a method for diagnosing cancer and predicting cancer types using a method for extracting methylated nucleic acids from a biospecimen, generating vectorized data of nucleic acid fragments based on aligned reads by obtaining sequence information, and then inputting the data into a trained artificial intelligence model so as to analyze a calculated value.

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 s: 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 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: 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 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 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: A method for determining copy number variation in a mixture of nucleic acids, which are known or believed to be different in terms of the amount of one or more target sequences. The method for determining variation may be used to chromosomal copy number variation which is associated with or believed to be associated with fetal diseases. Chromosomal copy number variations that may be determined according to the method may include trisomy and monosomy of any one or more of chromosomes 1-22, X and Y, polysomy for the full-length nucleic acid sequence, and deletion and/or duplication of any one or more sequence fragments of chromosomes, and thus the method is useful for the analysis of fetal gender and copy number variation.