Pipeline for retrieval of COVID-19 immune signatures

Abstract

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.