Please use this identifier to cite or link to this item:
http://hdl.handle.net/20.500.11861/7397
Title: | A Robust and Generalizable Immune-Related Signature for Sepsis Diagnostics |
Authors: | Yang, Yueran Zhang, Yu Li, Shuai Zheng, Xubin Wong, Man-Hon Prof. LEUNG Kwong Sak Cheng, Lixin |
Issue Date: | 2022 |
Source: | IEEE/ACM Transactions on Computational Biology and Bioinformatics, 2022, vol. 19(6), pp. 3246-3254. |
Journal: | IEEE/ACM Transactions on Computational Biology and Bioinformatics |
Abstract: | High-throughput sequencing can detect tens of thousands of genes in parallel, providing opportunities for improving the diagnostic accuracy of multiple diseases including sepsis, which is an aggressive inflammatory response to infection that can cause organ failure and death. Early screening of sepsis is essential in clinic, but no effective diagnostic biomarkers are available yet. Here, we present a novel method, Recurrent Logistic Regression, to identify diagnostic biomarkers for sepsis from the blood transcriptome data. A panel including five immune-related genes, LRRN3, IL2RB, FCER1A, TLR5, and S100A12, are determined as diagnostic biomarkers (LIFTS) for sepsis. LIFTS discriminates patients with sepsis from normal controls in high accuracy (AUROC = 0.9959 on average; IC = [0.9722-1.0]) on nine validation cohorts across three independent platforms, which outperforms existing markers. Our analysis determined an accurate prediction model and reproducible transcriptome biomarkers that can lay a foundation for clinical diagnostic tests and biological mechanistic studies. |
Type: | Peer Reviewed Journal Article |
URI: | http://hdl.handle.net/20.500.11861/7397 |
ISSN: | 1545-5963 1557-9964 |
DOI: | 10.1109/TCBB.2021.3107874 |
Appears in Collections: | Applied Data Science - Publication |
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