PGxCorpus: a Manually Annotated Corpus for Pharmacogenomics

Legrand, Joël; Gogdemir, Romain; Bousquet, Cédric; Dalleau, Kévin; Devignes, Marie‐Dominique; Digan, William; Lee, Chia‐Ju; Ndiaye, Ndeye-Coumba; Petitpain, Nadine; Ringot, Patrice; Smaïl-Tabbone, Malika; Toussaint, Yannick; Coulet, Adrien

doi:10.1101/534388

Cited by 4 publications

(9 citation statements)

References 45 publications

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“…1 -Example of sentence with four named entities and a relation between two of them. This relation is an extract from PGxCorpus (Legrand et al, 2020). posed of two or more named entities and a label that type the association between them.…”

Section: Relation Extraction Taskmentioning

confidence: 99%

“…PGxCorpus is a corpora of pharmacogenomic relationships between genomic factors and drugs or drug responses (Legrand et al, 2020). It is composed of 2,875 examples, distributed over 7 different types of relations (isAssociatedWith, influences, causes, decreases, increases, treats, isEquivalentTo).…”

Section: Two Example Corpora With Annotated Relationsmentioning

confidence: 99%

“…With PGxCorpus Results obtained with the fine-tuning strategy and PGxCorpus are presented in Table 6. The first line reports SOTA results from (Legrand et al, 2020), obtained with a simple MCNN, without any BERT pre-trained model.…”

Section: Fine-tuning Strategymentioning

confidence: 99%

“…Moreover, we explore strengthening BERT with structural information using sentence segmentation as post-processing, which is -up to our knowledge-an original approach. We tested on two benchmarks biomedical corpora ChemProt (Kringelum et al, 2016) and PGxCorpus (Legrand et al, 2020) .…”

Section: Introductionmentioning

confidence: 99%

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Experiments on transfer learning architectures for biomedical relation extraction

Hafiane¹,

Legrand²,

Toussaint³

et al. 2020

Preprint

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Relation extraction (RE) consists in identifying and structuring automatically relations of interest from texts. Recently, BERT improved the top performances for several NLP tasks, including RE. However, the best way to use BERT, within a machine learning architecture, and within a transfer learning strategy is still an open question since it is highly dependent on each specific task and domain. Here, we explore various BERT-based architectures and transfer learning strategies (i.e., frozen or fine-tuned) for the task of biomedical RE on two corpora. Among tested architectures and strategies, our *BERT-segMCNN with fine-tuning reaches performances higher than the state-of-the-art on the two corpora (1.73 % and 32.77 % absolute improvement on ChemProt and PGx-Corpus corpora respectively). More generally, our experiments illustrate the expected interest of fine-tuning with BERT, but also the unexplored advantage of using structural information (with sentence segmentation), in addition to the context classically leveraged by BERT.

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Section: Relation Extraction Taskmentioning

confidence: 99%

Section: Two Example Corpora With Annotated Relationsmentioning

confidence: 99%

Section: Fine-tuning Strategymentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Experiments on transfer learning architectures for biomedical relation extraction

Hafiane¹,

Legrand²,

Toussaint³

et al. 2020

Preprint

Self Cite

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show abstract

“…Nevertheless, the notion of a relation, what it "means", is inherently ambiguous [35]. Many efforts have been devoted to biomedical relation extraction, whose goal is to discover valuable knowledge about proteins, drugs, diseases, genes, adverse effects, and other biological interactions from unstructured free text [36].…”

Section: Related Workmentioning

confidence: 99%

How to Adapt Deep Learning Models to a New Domain: The Case of Biomedical Relation Extraction

Peña-Torres

Gutiérrez

Bucheli

et al. 2019

TecnoL.

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In this article, we study the relation extraction problem from Natural Language Processing (NLP) implementing a domain adaptation setting without external resources. We trained a Deep Learning (DL) model for Relation Extraction (RE), which extracts semantic relations in the biomedical domain. However, can the model be applied to different domains? The model should be adaptable to automatically extract relationships across different domains using the DL network. Completely training DL models in a short time is impractical because the models should quickly adapt to different datasets in several domains without delay. Therefore, adaptation is crucial for intelligent systems, where changing factors and unanticipated perturbations are common. In this study, we present a detailed analysis of the problem, as well as preliminary experimentation, results, and their evaluation.

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Extract antibody and antigen names from biomedical literature

et al. 2022

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Background The roles of antibody and antigen are indispensable in targeted diagnosis, therapy, and biomedical discovery. On top of that, massive numbers of new scientific articles about antibodies and/or antigens are published each year, which is a precious knowledge resource but has yet been exploited to its full potential. We, therefore, aim to develop a biomedical natural language processing tool that can automatically identify antibody and antigen entities from articles. Results We first annotated an antibody-antigen corpus including 3210 relevant PubMed abstracts using a semi-automatic approach. The Inter-Annotator Agreement score of 3 annotators ranges from 91.46 to 94.31%, indicating that the annotations are consistent and the corpus is reliable. We then used the corpus to develop and optimize BiLSTM-CRF-based and BioBERT-based models. The models achieved overall F1 scores of 62.49% and 81.44%, respectively, which showed potential for newly studied entities. The two models served as foundation for development of a named entity recognition (NER) tool that automatically recognizes antibody and antigen names from biomedical literature. Conclusions Our antibody-antigen NER models enable users to automatically extract antibody and antigen names from scientific articles without manually scanning through vast amounts of data and information in the literature. The output of NER can be used to automatically populate antibody-antigen databases, support antibody validation, and facilitate researchers with the most appropriate antibodies of interest. The packaged NER model is available at https://github.com/TrangDinh44/ABAG_BioBERT.git.

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PGxCorpus: a Manually Annotated Corpus for Pharmacogenomics

Cited by 4 publications

References 45 publications

Experiments on transfer learning architectures for biomedical relation extraction

Experiments on transfer learning architectures for biomedical relation extraction

How to Adapt Deep Learning Models to a New Domain: The Case of Biomedical Relation Extraction

Extract antibody and antigen names from biomedical literature

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