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...

Descripción completa

Detalles Bibliográficos
Autores principales: Shi, Songchang, Pan, Xiaobin, Zhang, Lihui, Wang, Xincai, Zhuang, Yingfeng, Lin, Xingsheng, Shi, Songjing, Zheng, Jianzhang, Lin, Wei
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2022
Materias:
Genetics
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9490444/
https://www.ncbi.nlm.nih.gov/pubmed/36159979
http://dx.doi.org/10.3389/fgene.2022.979529

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9490444/
https://www.ncbi.nlm.nih.gov/pubmed/36159979
http://dx.doi.org/10.3389/fgene.2022.979529

Ejemplares similares