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Title

Designing an Optimal Expansion Method to Improve the Recall of a Genomic Variant Curation-Support Service

Authors
Published in Studies in health technology and informatics. 2022, vol. 294, p. 839-843
Abstract The importance of genomic data for health is rapidly growing but accessing and gathering information about variants from different sources is hindered by highly heterogeneous representations of variants, as outlined by clinical associations (AMP/ASCO/CAP) in their recommendations. To enable a smooth and effective retrieval of variant-containing documents from different resources, we developed a tool (https://goldorak.hesge.ch/synvar/) that generates for any given SNP - including variant not present in existing databases - its corresponding description at the genome, transcript and protein levels. It provides variant descriptions in the HGVS format as well as in many non-standard formats found in the literature along with database identifiers. We present the SynVar service and evaluate its impact on the recall of a genomic variant curation-support service. Using SynVar to search variants in the literature enables to increase the recall by +133.8% without a strong impact on precision (i.e. 93%).
Keywords Biomedical literatureGenomic variantPrecision medicineDatabases, FactualGenomics
Identifiers
PMID: 35612222
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Structures
Research group DS4DH - Data Science for Digital Health (1035)
Projects
European Commission: CINECA
Canadian Institute of Health Research (CIHR) : 404896
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(ISO format)
MOTTAZ, Anais et al. Designing an Optimal Expansion Method to Improve the Recall of a Genomic Variant Curation-Support Service. In: Studies in health technology and informatics, 2022, vol. 294, p. 839-843. doi: 10.3233/SHTI220603 https://archive-ouverte.unige.ch/unige:162728

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Deposited on : 2022-08-19

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