Briefings in bioinformatics · 2026
Machine learning methodology using a masked neural network for robust genetic risk score calculation from noisy and missing data.
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Squires Steven, Weedon Michael N, Oram Richard A
Аннотация
PURPOSE: 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 are missing, noisy or both.
APPROACH: We developed a neural network approach, named masked-multi-layer perceptron (MLP), for robust GRS calculation trained on a set of GRS scores calculated on clean data. The masked-MLP includes additional input data and has noise inserted during training, both which make the model more robust.
RESULTS: A GRS for type 1 diabetes (T1D) calculated on input data with 10% of the data corrupted had a Spearman rank correlation to the clean GRS of 0.669 (0.665-0.674) while the equivalent for the masked-MLP was 0.951 (0.950-0.952). For the same data, the area under the receiver operating characteristic curve for separation of T1D from population samples fell from 0.919 (0.904-0.932) to 0.808 (0.787-0.827) for the GRS while the masked-MLP fell to 0.910 (0.895-0.924).
CONCLUSIONS: The masked-MLP was more robust to noise when calculating a GRS than using standard approaches. Our approach has the potential to enable GRS calculations which are more robust to noise.
APPROACH: We developed a neural network approach, named masked-multi-layer perceptron (MLP), for robust GRS calculation trained on a set of GRS scores calculated on clean data. The masked-MLP includes additional input data and has noise inserted during training, both which make the model more robust.
RESULTS: A GRS for type 1 diabetes (T1D) calculated on input data with 10% of the data corrupted had a Spearman rank correlation to the clean GRS of 0.669 (0.665-0.674) while the equivalent for the masked-MLP was 0.951 (0.950-0.952). For the same data, the area under the receiver operating characteristic curve for separation of T1D from population samples fell from 0.919 (0.904-0.932) to 0.808 (0.787-0.827) for the GRS while the masked-MLP fell to 0.910 (0.895-0.924).
CONCLUSIONS: The masked-MLP was more robust to noise when calculating a GRS than using standard approaches. Our approach has the potential to enable GRS calculations which are more robust to noise.