A curated collection of human vaccination response signatures

Smith, Kyle; Chawla, Daniel G.; Bk, Dhillon; Z, Ji; Vita, Randi; E, van der Leest; Weng, Jing Yi Jessica; Tang, Ernest; Abid, Amani; Peters, Bjoern; Re, Hancock; Floratos, Aristidis; Kleinstein, Steven H.

doi:10.1101/2021.04.15.439017

Cited by 2 publications

References 26 publications

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Pipeline for retrieval of COVID-19 immune signatures

Newton

Chartash

Kleinstein

et al. 2021

Preprint

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Objective: The accelerating pace of biomedical publication has made retrieving papers and extracting specific comprehensive scientific information a key challenge. A timely example of such a challenge is to retrieve the subset of papers that report on immune signatures (coherent sets of biomarkers) to understand the immune response mechanisms which drive differential SARS-CoV-2 infection outcomes. A systematic and scalable approach is needed to identify and extract COVID-19 immune signatures in a structured and machine-readable format. Materials and Methods: We used SPECTER embeddings with SVM classifiers to automatically identify papers containing immune signatures. A generic web platform was used to manually screen papers and allow anonymous submission. Results: We demonstrate a classifier that retrieves papers with human COVID-19 immune signatures with a positive predictive value of 86%. Semi-automated queries to the corresponding authors of these publications requesting signature information achieved a 31% response rate. This demonstrates the efficacy of using a SVM classifier with document embeddings of the abstract and title, to retrieve papers with scientifically salient information, even when that information is rarely present in the abstract. Additionally, classification based on the embeddings identified the type of immune signature (e.g., gene expression vs. other types of profiling) with a positive predictive value of 74%. Conclusions: Coupling a classifier based on document embeddings with direct author engagement offers a promising pathway to build a semi-structured representation of scientifically relevant information. Through this approach, partially automated literature mining can help rapidly create semi-structured knowledge repositories for automatic analysis of emerging health threats.

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Pipeline for retrieval of COVID-19 immune signatures

Newton

Chartash

Kleinstein

et al. 2021

Preprint

Self Cite

View full text Add to dashboard Cite

show abstract

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

et al. 2023

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Background The 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. Implementation We 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. Results We 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. Conclusions Our 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.

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A curated collection of human vaccination response signatures

Cited by 2 publications

References 26 publications

Pipeline for retrieval of COVID-19 immune signatures

Pipeline for retrieval of COVID-19 immune signatures

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

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