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

Mottaz, Anaïs; Pasche, Emilie; Michel, Pierre-André; Mottin, Luc; Teodoro, Douglas; Ruch, Patrick

doi:10.3233/shti220603

Cited by 2 publications

(4 citation statements)

References 12 publications

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“…However, some of the chosen experimental settings are likely to underestimate such a statement. Indeed, while the search for variants is benefiting from a quite powerful synonym generation engine, so-called SynVar ( 14 ), which can associate many synonyms of variants (e.g. BRAF :V600E, BRAF :Val600Glu, and BRAF :1799T>A), the variability of the gene (or gene product) names has not been similarly exploited.…”

Section: Discussionmentioning

confidence: 99%

“…there are dozens of expressions to represent a given variant, including expressions at the genomic, protein or transcript levels), and there is no universal terminology for describing variants. The Variomes search engine relies on SynVar ( 14 ), a dedicated variant expansion system to expand the query and retrieve documents mentioning the variant in any of its forms. The system is able to compute from a given single nucleotide polymorphism (SNP) all the different description formats, including protein, transcript and genome levels and syntactic variations as found in the literature.…”

Section: Methodsmentioning

confidence: 99%

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Assessing the use of supplementary materials to improve genomic variant discovery

et al. 2023

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The curation of genomic variants requires collecting evidence not only in variant knowledge bases but also in the literature. However, some variants result in no match when searched in the scientific literature. Indeed, it has been reported that a significant subset of information related to genomic variants are not reported in the full text, but only in the supplementary materials associated with a publication. In the study, we present an evaluation of the use of supplementary data (SD) to improve the retrieval of relevant scientific publications for variant curation. Our experiments show that searching SD enables to significantly increase the volume of documents retrieved for a variant, thus reducing by ∼63% the number of variants for which no match is found in the scientific literature. SD thus represent a paramount source of information for curating variants of unknown significance and should receive more attention by global research infrastructures, which maintain literature search engines. Database URL https://www.expasy.org/resources/variomes

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Section: Discussionmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Assessing the use of supplementary materials to improve genomic variant discovery

et al. 2023

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“…To address this paradigm, (semi-)automatic curation systems based on text mining and natural language processing (NLP) technologies have been developed to support review and annotation of large literature corpora [ 12 – 22 ]. These systems support the identification and ranking of relevant articles, the categorization of the selected documents in classes and subclasses for reviewing procedures, and enable information extraction from text passages (e.g., identification of disease passages).…”

Section: Introductionmentioning

confidence: 99%

“…SIBiLS [ 20 ] provide an optimized search engine in the biological literature by augmenting its contents with keywords and standardized entities. Variomes [ 22 ] are a system that can perform triage of publication to support evidence-based decision. Finally, PubTerm [ 13 ] enables the organization of abstracts by terms, using the co-occurrence of terms or by specific phrases, among others, to facilitate the biomedical curation process.…”

Section: Introductionmentioning

confidence: 99%

Ensemble of deep learning language models to support the creation of living systematic reviews for the COVID-19 literature

Knafou

Haas²,

Borissov

et al. 2023

Syst Rev

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Background The COVID-19 pandemic has led to an unprecedented amount of scientific publications, growing at a pace never seen before. Multiple living systematic reviews have been developed to assist professionals with up-to-date and trustworthy health information, but it is increasingly challenging for systematic reviewers to keep up with the evidence in electronic databases. We aimed to investigate deep learning-based machine learning algorithms to classify COVID-19-related publications to help scale up the epidemiological curation process. Methods In this retrospective study, five different pre-trained deep learning-based language models were fine-tuned on a dataset of 6365 publications manually classified into two classes, three subclasses, and 22 sub-subclasses relevant for epidemiological triage purposes. In a k-fold cross-validation setting, each standalone model was assessed on a classification task and compared against an ensemble, which takes the standalone model predictions as input and uses different strategies to infer the optimal article class. A ranking task was also considered, in which the model outputs a ranked list of sub-subclasses associated with the article. Results The ensemble model significantly outperformed the standalone classifiers, achieving a F1-score of 89.2 at the class level of the classification task. The difference between the standalone and ensemble models increases at the sub-subclass level, where the ensemble reaches a micro F1-score of 70% against 67% for the best-performing standalone model. For the ranking task, the ensemble obtained the highest recall@3, with a performance of 89%. Using an unanimity voting rule, the ensemble can provide predictions with higher confidence on a subset of the data, achieving detection of original papers with a F1-score up to 97% on a subset of 80% of the collection instead of 93% on the whole dataset. Conclusion This study shows the potential of using deep learning language models to perform triage of COVID-19 references efficiently and support epidemiological curation and review. The ensemble consistently and significantly outperforms any standalone model. Fine-tuning the voting strategy thresholds is an interesting alternative to annotate a subset with higher predictive confidence.

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

Cited by 2 publications

References 12 publications

Assessing the use of supplementary materials to improve genomic variant discovery

Assessing the use of supplementary materials to improve genomic variant discovery

Ensemble of deep learning language models to support the creation of living systematic reviews for the COVID-19 literature

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