--- annotations_creators: - expert-generated language: - en language_creators: - machine-generated license: - cc-by-nc-sa-4.0 multilinguality: - monolingual pretty_name: Detection of Unlimited Variant Ensemble in Literature (DUVEL) size_categories: - 1KT of NELF and c.824G>A; @VARIANT$ of TACR3).', 'pmcid': 3888818, 'gene1': 'KAL1;55445', 'gene2': 'NELF;10648', 'variant1': 'c.757G>A;tmVar:c|SUB|G|757|A;HGVS:c.757G>A;VariantGroup:3;CorrespondingGene:26012;RS#:142726563;CA#:5370407', 'variant2': 'p.Trp275X;tmVar:p|SUB|W|275|X;HGVS:p.W275X;VariantGroup:1;CorrespondingGene:6870;RS#:144292455;CA#:144871', 'label': 0 } ``` ### Data Fields - `sentence`: *string*, text containing the entities masked with either @GENE$ for the gene type or @VARIANT$ for the mutation type. The text can be either single or cross-sentence, but no longer than 256 tokens according to the PubMedBERT tokenizer (see [PubMedBERT](https://huggingface.co/microsoft/BiomedNLP-PubMedBERT-base-uncased-abstract-fulltext)). - `pmcid`: *int*, PubMed Central identifier of the article from which the text was extracted (https://www.ncbi.nlm.nih.gov/pmc/) - `gene1`: *string*, first gene mention as it appears in the text and internal identifier. - `gene2`: *string*, second gene mention as it appears in the text and internal identifier. - `variant1`: *string*, first variant mention as it appears in the text, with its normalized form, HGVS form (https://varnomen.hgvs.org/), gene where it occurs, and eventually variation identifier is available. - `variant2`: *string*, second variant mention as it appears in the text, with its normalized form, HGVS form (https://varnomen.hgvs.org/), gene where it occurs, and eventually variation identifier is available. - `label`: *int*, class of the instance, 0 if there is no relation between the entities, 1 if there is. ### Data Splits Dataset is split between train, dev and test sets. Splitting has been done with a stratified split based on the labels in order to maintain a similar distribution (around 9.4% of positive class). | | train | test | dev | |--------------------------------------------|------:|-----:|-----:| | Total number of instances | 6553 | 1689 | 200 | | Number of positive instances | 616 | 159 | 19 | | Total number of articles | 79 | 75 | 51 | | Number of articles with positive instances | 61 | 51 | 12 | | Number of articles with negative instances | 78 | 73 | 50 | ## Dataset Creation ### Curation Rationale The curation of oligogenic variant combinations requires high expertise and time, while the number of genetic studies have increased across the years, especially with the apparition of the next-generation sequencing technologies. This dataset aims to support such curation by extracting potential candidates directly from the text. ### Source Data #### Initial Data Collection and Normalization Scientific articles containing oligogenic variant combinations potentially causing genetic diseases were retrieved from [OLIDA](https://olida.ibsquare.be), the OLIgogenic diseases DAtabase. Articles were filtered to keep only those containing at least one digenic variant combination, i.e. combination between two genes and at least one variant in each gene. The articles were then pre-annotated with the help of PubTator API (https://www.ncbi.nlm.nih.gov/research/pubtator/api.html) to obtain the full-text articles with the genes and variants identified. Candidates were created by extracting all the text portion (both single and cross-sentence) containing two gene and two variant mentions with a maximum length of 256 tokens, as tokenized by the PubMedBERT tokenizer (see [PubMedBERT](https://huggingface.co/microsoft/BiomedNLP-PubMedBERT-base-uncased-abstract-fulltext)). Text containing tables or incomplete sentences were excluded during the annotation process. #### Who are the source language producers? The dataset is machine-generated, as the full annotated text of the article is retrieved from the PubTator API and then the relevant text containing two genes and two variants are generated through python scripts. ### Annotations The annotation was done with the ALAMBIC platform, with an Active Learning (AL) setting (see [Nachtegael 2023](https://aclanthology.org/2023.eacl-demo.14)). #### Annotation process 1500 samples were randomly selected to be labelled, with 1000 samples for the test set and 500 as seed for the AL process. 9 iterations of AL selection of 500 samples with the Margin Sampling strategy was conducted with PubMEdBERT as the model used for the selection (see [PubMedBERT](https://huggingface.co/microsoft/BiomedNLP-PubMedBERT-base-uncased-abstract-fulltext)). The annotation limit was initially set at 6000 samples, but was exceeded due to several restarts of the process due to technical errors. The annotator had access to the genes and variants, the PMCID of the article the text was extracted from and the text with the masked entities. One out of three possible classes is given to each variant combination candidate : - *0* for the absence of a digenic variant combination relation in the text. - *1* for the presence of a digenic variant combination relation. The genes and the variants need to be relating to each other for there to be a valid relation. If the entities are involved in an alleged digenic relation according to OLIDA, but the syntactic aspects of the text showed no clear relation between the entities, then the text contains no relation. The combination needs to be carried by at least one individual, as depicted in the text. - *-1* if the candidate is not valid. A candidate can be deemed as invalid if one of the entities is not a valid entity, i.e. not a valid gene name or mutation, or the text contains an unfinished sentence or invalid sentence, i.e. with part of the text being a table. It must be noted that while the articles were filtered for those containing digenic variant combinations, it is possible to also find oligogenic variant combinations involving more than two genes and/or two variants. In that case, a subset of those variant combinations, i.e. two gene-variant pairs which are connected in the text and are part of the variant combination, were considered as a valid digenic variant combinations and classified them as class *1*. #### Who are the annotators? Annotation was done by Charlotte Nachtegael, one of the author and curator of OLIDA, with a substantial background in genetics and molecular biology. ### Personal and Sensitive Information None. ## Considerations for Using the Data ### Social Impact of Dataset The dataset should help to the curation of complex genetic diseases, contributing to the research of such medical problems. It should not, at the moment, but used exclusively for support of the curation and not as the curation iteself of oligogenic/digenic variant combinations. ### Discussion of Biases Some diseases are more studied/known as oligogenic, thus the variants and genes could be biased towards those gene panels more well-known. Moreover, some articles are more represented in the dataset than others because they had more genes and/or variants in the text than others. The named entity recognition step was also done automatically, so it could be possible that some entities were not recognized and thus ignored when creating the candidates. When errors were encountered during the annotation process, the candidates were excluded from the dataset. ### Other Known Limitations None. ## Additional Information ### Dataset Curators This work was supported by the Service Public de Wallonie Recherche by DIGITALWALLONIA4.AI [2010235—ARIAC] - Charlotte Nachtegael, Université Libre de Bruxelles, Belgium ### Licensing Information This dataset is under the Creative Commons Attribution Non Commercial Share Alike 4.0 license. ### Citation Information TBA ```bib @article{DUVEL_2023, author = {}, title = {}, journal = {}, year = {2023} } ``` ### Contributions Thanks to Barbara Gravel and Sofia Papadimitriou for their initial work with OLIDA. Thanks to Jacopo de Stefani, Anthony Cnudde and Tom Lenaerts for their help with the experimental design and writing of the paper for DUVEL.