The DOI matches its Crossref record; 19 of the 19 references it cites were found in a registry or an index.
Improved pathogenicity prediction for rare human missense variants
Open accessAbstract
The success of personalized genomic medicine depends on our ability to assess the pathogenicity of rare human variants, including the important class of missense variation. There are many challenges in training accurate computational systems, e.g., in finding the balance between quantity, quality, and bias in the variant sets used as training examples and avoiding predictive features that can accentuate the effects of bias. Here, we describe VARITY, which judiciously exploits a larger reservoir of training examples with uncertain accuracy and representativity. To limit circularity and bias, VARITY excludes features informed by variant annotation and protein identity. To provide a rationale for each prediction, we quantified the contribution of features and feature combinations to the pathogenicity inference of each variant. VARITY outperformed all previous computational methods evaluated, identifying at least 10% more pathogenic variants at thresholds achieving high (90% precision) stringency.
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BackgroundMethodInfluential
Cited by
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Metadata
- Authors
- Wu, Y., Liu, H., Li, R., Sun, S., Weile, J., & Roth, F. P.
- Venue
- The American Journal of Human Genetics
- Publisher
- Elsevier BV
- Published
- 2021-10
- Volume
- 108
- Issue
- 10
- Pages
- 1891-1906
- ISSN
- 0002-9297 (ISSN-L)
- OpenAlex
- W3199639770
- Cited by
- 164
- References
- 19
- FWCI
- 11.58
- Type
- journal article
- Language
- en
- Open access
- hybrid
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- Wu, Y., Liu, H., Li, R., Sun, S., Weile, J., & Roth, F. P. (2021). Improved pathogenicity prediction for rare human missense variants. The American Journal of Human Genetics. https://doi.org/10.1016/j.ajhg.2021.08.012
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