USING MACHINE LEARNING MODELS FOR DETECTING MINIMUM RESIDUAL DISEASE (MRD) IN A SUBJECT

• Main Authors: Bilke, Sven, Song, Fan, Dutta, Anindita, Zhu, Yunjiao, Hashemidoulabi, Seyedmohammadjafar, Han, James
• Format: Patent
• Language: English
• Published: 19.06.2025
• Subjects: BEER; BIOCHEMISTRY; CHEMISTRY; COMPOSITIONS OR TEST PAPERS THEREFOR; CONDITION-RESPONSIVE CONTROL IN MICROBIOLOGICAL ORENZYMOLOGICAL PROCESSES; ENZYMOLOGY; INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTEDFOR SPECIFIC APPLICATION FIELDS; MEASURING OR TESTING PROCESSES INVOLVING ENZYMES, NUCLEICACIDS OR MICROORGANISMS; METALLURGY; MICROBIOLOGY; MUTATION OR GENETIC ENGINEERING; PHYSICS; PROCESSES OF PREPARING SUCH COMPOSITIONS; SPIRITS; VINEGAR; WINE

This disclosure describes methods, non-transitory computer-readable media, and systems that detect minimal residual disease (MRD) within a sample of interest. For example, in some cases, the disclosed systems identify, for an initial genomic sample of a subject infected with cancer, a tumor fingerprint comprising variants at a target genomic region. The disclosed systems further determine, for a sample of interest of the subject, a set of sample of interest nucleotide reads associated with the target genomic region. The disclosed system process the set of sample of interest nucleotide reads using a first machine learning model and process panel of normals nucleotide reads using one or more additional machine learning models. The disclosed systems compare scores determined from the outputs of the machine learning models to predict whether the sample of interest has minimal residual disease related to the cancer.