An application based on bioinformatics and machine learning for risk prediction of sepsis at first clinical presentation using transcriptomic data

An application based on bioinformatics and machine learning for risk prediction of sepsis at first clinical presentation using transcriptomic data

Background: Linking genotypic changes to phenotypic traits based on machine learning methods has various challenges. In this study, we developed a workflow based on bioinformatics and machine learning methods using transcriptomic data for sepsis obtained at the first clinical presentation for predic...

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Bibliographic Details
Main Authors: Songchang Shi, Xiaobin Pan, Lihui Zhang, Xincai Wang, Yingfeng Zhuang, Xingsheng Lin, Songjing Shi, Jianzhang Zheng, Wei Lin
Format: Article
Language:English
Published: Frontiers Media S.A. 2022-09-01
Series:Frontiers in Genetics
Subjects:
application
prediction
transcriptome
sepsis
machine learning
Online Access:https://www.frontiersin.org/articles/10.3389/fgene.2022.979529/full

Internet

https://www.frontiersin.org/articles/10.3389/fgene.2022.979529/full

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