Invention Publication
- Patent Title: DETECTING LOSS OF HETEROZYGOSITY IN HLA ALLELES USING MACHINE-LEARNING MODELS
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Application No.: US18556143Application Date: 2022-04-21
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Publication No.: US20240185952A1Publication Date: 2024-06-06
- Inventor: Rachel Marty PYKE , Dattatreya MELLACHERUVU , Steven DEA , Charles Wilbur ABBOTT , Simo V. ZHANG , Eric LEVY , John WEST , Richard CHEN , Sean Michael BOYLE
- Applicant: Personalis, Inc.
- Applicant Address: US CA Fremont
- Assignee: Personalis, Inc.
- Current Assignee: Personalis, Inc.
- Current Assignee Address: US CA Fremont
- International Application: PCT/US2022/025752 2022.04.21
- Date entered country: 2023-10-19
- Main IPC: G16B20/20
- IPC: G16B20/20 ; G16B20/10 ; G16B40/20

Abstract:
A method of detecting loss of heterozygosity in HLA alleles is provided. The method can include accessing a trained machine-learning model, which was trained using a training data set that included at least a training data set that includes an adjusted B allele frequency that represents a ratio between a first B allele frequency of heterozygous alleles in the tumor sample that correspond to the genomic region and a second B allele frequency of heterozygous alleles in the genomic region and associated with one or more control samples. The method can also include using the machine-learning model to generate a result corresponding to a probability of whether a loss of heterozygosity exists in an HLA allele identified in the biological sample of the particular subject by processing the sequence data using the machine-learning model.
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