Article

Machine learning methodology using a masked neural network for robust genetic risk score calculation from noisy and missing data

2026-05-20

Abstract excerpt

<h4> A bstract </h4> <h4>Purpose</h4> Genetic risk scores (GRSs) are summaries of genetic data that can improve prediction of disease risk and progression. GRSs are increasing available but rely on high quality input data to produce good output results; with noisy or missing inputs the GRS may be inaccurate. We aimed to develop a method to produce a robust estimate of the GRS when input data is missing, noisy...

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Literature Corpus work: 3a61446d-eb1d-59de-bd05-59e64f4ef19b
DOI: 10.64898/2026.05.18.25341725

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