A pipeline for the retrieval and extraction of domain-specific information with application to COVID-19 immune signatures

BMC Bioinformatics

Software

Steven H. Kleinstein¹  Adam J. H. Newton²  Robert A. McDougal³  David Chartash⁴ 

[1] Department of Pathology, Yale School of Medicine, Yale University, 06511, New Haven, CT, USA;Department of Immunobiology, Yale School of Medicine, Yale University, 06511, New Haven, CT, USA;Program in Computational Biology and Bioinformatics, Yale University, 06511, New Haven, CT, USA;Department of Physiology and Pharmacology, SUNY Downstate Health Sciences University, 11203, Brooklyn, NY, USA;Yale Center for Medical Informatics, Yale School of Medicine, Yale University, 06511, New Haven, CT, USA;Department of Biostatistics, Yale School of Public Health, Yale University, 06511, New Haven, CT, USA;Department of Pathology, Yale School of Medicine, Yale University, 06511, New Haven, CT, USA;Yale Center for Medical Informatics, Yale School of Medicine, Yale University, 06511, New Haven, CT, USA;Department of Biostatistics, Yale School of Public Health, Yale University, 06511, New Haven, CT, USA;Program in Computational Biology and Bioinformatics, Yale University, 06511, New Haven, CT, USA;Yale Center for Medical Informatics, Yale School of Medicine, Yale University, 06511, New Haven, CT, USA;Department of Biostatistics, Yale School of Public Health, Yale University, 06511, New Haven, CT, USA;School of Medicine, University College Dublin - National University of Ireland, Dublin, Co. Dublin, Republic of Ireland;

关键词: COVID-19;    Biomarkers;    Data mining;    Immunity;    Knowledge bases;   

DOI  :  10.1186/s12859-023-05397-8

 received in 2023-01-26, accepted in 2023-06-23,  发布年份 2023

来源: Springer

【 摘 要 】

BackgroundThe accelerating pace of biomedical publication has made it impractical to manually, systematically identify papers containing specific information and extract this information. This is especially challenging when the information itself resides beyond titles or abstracts. For emerging science, with a limited set of known papers of interest and an incomplete information model, this is of pressing concern. A timely example in retrospect is the identification of immune signatures (coherent sets of biomarkers) driving differential SARS-CoV-2 infection outcomes.ImplementationWe built a classifier to identify papers containing domain-specific information from the document embeddings of the title and abstract. To train this classifier with limited data, we developed an iterative process leveraging pre-trained SPECTER document embeddings, SVM classifiers and web-enabled expert review to iteratively augment the training set. This training set was then used to create a classifier to identify papers containing domain-specific information. Finally, information was extracted from these papers through a semi-automated system that directly solicited the paper authors to respond via a web-based form.ResultsWe demonstrate a classifier that retrieves papers with human COVID-19 immune signatures with a positive predictive value of 86%. The type of immune signature (e.g., gene expression vs. other types of profiling) was also identified with a positive predictive value of 74%. Semi-automated queries to the corresponding authors of these publications requesting signature information achieved a 31% response rate.ConclusionsOur results demonstrate the efficacy of using a SVM classifier with document embeddings of the title and abstract, to retrieve papers with domain-specific information, even when that information is rarely present in the abstract. Targeted author engagement based on classifier predictions offers a promising pathway to build a semi-structured representation of such information. Through this approach, partially automated literature mining can help rapidly create semi-structured knowledge repositories for automatic analysis of emerging health threats.

【 授权许可】

CC BY   
© The Author(s) 2023

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