MACHINE-LEARNING MODEL FOR REFINING STRUCTURAL VARIANT CALLS

This disclosure describes methods, non-transitory computer readable media, and systems that can utilize a machine-learning model to refine structural variant calls of a call generation model. For example, the disclosed systems can train and utilize a structural variant refinement machine-learning mo...

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Bibliographic Details
Main Authors Nariai, Naoki, Parnaby, Gavin Derek, Chari, Sujai
Format Patent
LanguageEnglish
Published 11.04.2024
Subjects
CALCULATING
COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
COMPUTING
COUNTING
INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTEDFOR SPECIFIC APPLICATION FIELDS
PHYSICS
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Summary:This disclosure describes methods, non-transitory computer readable media, and systems that can utilize a machine-learning model to refine structural variant calls of a call generation model. For example, the disclosed systems can train and utilize a structural variant refinement machine-learning model to reduce false positives and/or false negatives. Indeed, the disclosed systems can improve or refine structural variant calls (e.g., between 50-200 base pairs in length) determined by a call generation model by training and utilizing the structural variant refinement machine-learning model. As disclosed, the systems can determine sequencing metrics and can customize training data for a structural variant refinement machine-learning model to generate modified structural variant calls.
Bibliography:Application Number: US202318476232