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自适应最佳子集选择算法和遗传算法辅助的集成学习方法确定了新冠肺炎患者的稳健严重程度评分。

Adaptive best subset selection algorithm and genetic algorithm aided ensemble learning method identified a robust severity score of COVID-19 patients.

作者信息

Kong Weikaixin, Zhu Jie, Bi Suzhen, Huang Liting, Wu Peng, Zhu Su-Jie

机构信息

Institute for Molecular Medicine Finland (FIMM), HiLIFE University of Helsinki Helsinki Finland.

Institute of Translational Medicine, The Affiliated Hospital of Qingdao University, College of Medicine Qingdao University Qingdao China.

出版信息

Imeta. 2023 Jul 4;2(3):e126. doi: 10.1002/imt2.126. eCollection 2023 Aug.

DOI:10.1002/imt2.126
PMID:38867930
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC10989835/
Abstract

We used an integrated ensemble learning method to build a stable prediction model for severity in COVID-19 patients, which was validated in multicenter cohorts.

摘要

我们采用了一种集成的集成学习方法来构建针对新冠肺炎患者病情严重程度的稳定预测模型，该模型在多中心队列中得到了验证。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3e04/10989835/2de6267b6ff0/IMT2-2-e126-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3e04/10989835/3f915f077bde/IMT2-2-e126-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3e04/10989835/1b3d4789d8f7/IMT2-2-e126-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3e04/10989835/2de6267b6ff0/IMT2-2-e126-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3e04/10989835/3f915f077bde/IMT2-2-e126-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3e04/10989835/1b3d4789d8f7/IMT2-2-e126-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3e04/10989835/2de6267b6ff0/IMT2-2-e126-g004.jpg

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Single-cell analyses and host genetics highlight the role of innate immune cells in COVID-19 severity.单细胞分析和宿主遗传学突出了固有免疫细胞在 COVID-19 严重程度中的作用。
Nat Genet. 2023 May;55(5):753-767. doi: 10.1038/s41588-023-01375-1. Epub 2023 Apr 24.
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J Transl Med. 2024 Apr 15;22(1):353. doi: 10.1186/s12967-024-05138-2.
整合生物信息学分析鉴定缺血性脑卒中与 COVID-19 之间共同的免疫变化。
Front Immunol. 2023 Mar 8;14:1102281. doi: 10.3389/fimmu.2023.1102281. eCollection 2023.
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