Deep learning-based pathogenicity classifier for promoter single nucleotide variants (pSNVs)
US11861491B2 · kind B2 · utility
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| Filing date | Sep 20, 2019 |
| Grant date | Jan 2, 2024 |
| Priority date | — |
| Expiry date | Sep 19, 2042 |
Classification
- Technology area (CPC G)Physics
- CPC primaryG06N3/045
- WIPO fieldComputer technology
- WIPO sectorElectrical engineering
Abstract
We disclose computational models that alleviate the effects of human ascertainment biases in curated pathogenic non-coding variant databases by generating pathogenicity scores for variants occurring in the promoter regions (referred to herein as promoter single nucleotide variants (pSNVs)). We train deep learning networks (referred to herein as pathogenicity classifiers) using a semi-supervised approach to discriminate between a set of labeled benign variants and an unlabeled set of variants that were matched to remove biases.
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