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- W2992091241 abstract "Abstract Prediction methods have become an integral part of biomedical and biotechnological research. However, their clinical interpretations are largely based on biochemical or molecular data, but not clinical data. Here, we focus on improving the reliability and clinical applicability of prediction algorithms. We assembled and curated two large non-overlapping large databases of clinical phenotypes. These phenotypes were caused by missense variations in 44 and 63 genes associated with Mendelian diseases. We used these databases to establish and validate the model, allowing us to improve the predictions obtained from EVmutation, SNAP2 and PoPMuSiC 2.1. The predictions of clinical effects suffered from a lack of specificity, which appears to be the common constraint of all recently used prediction methods, although predictions mediated by these methods are associated with nearly absolute sensitivity. We introduced evidence-based tailoring of the default settings of the prediction methods; this tailoring substantially improved the prediction outcomes. Additionally, the comparisons of the clinically observed and theoretical variations led to the identification of large previously unreported pools of variations that were under negative selection during molecular evolution. The evolutionary variation analysis approach described here is the first to enable the highly specific identification of likely disease-causing missense variations that have not yet been associated with any clinical phenotype." @default.
- W2992091241 created "2019-12-13" @default.
- W2992091241 creator A5003574703 @default.
- W2992091241 creator A5047225751 @default.
- W2992091241 date "2019-12-09" @default.
- W2992091241 modified "2023-10-14" @default.
- W2992091241 title "Refinement of evolutionary medicine predictions based on clinical evidence for the manifestations of Mendelian diseases" @default.
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- W2992091241 doi "https://doi.org/10.1038/s41598-019-54976-4" @default.
- W2992091241 hasPubMedCentralId "https://www.ncbi.nlm.nih.gov/pmc/articles/6901466" @default.
- W2992091241 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/31819097" @default.
- W2992091241 hasPublicationYear "2019" @default.
- W2992091241 type Work @default.
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