Machine learning algorithm to characterize antimicrobial resistance associated with the International Space Station surface microbiome

被引：0

作者：

Pedro Madrigal

Nitin K. Singh

Jason M. Wood

Elena Gaudioso

Félix Hernández-del-Olmo

Christopher E. Mason

Kasthuri Venkateswaran

Afshin Beheshti

机构：

[1] University of Cambridge,Jeffrey Cheah Biomedical Centre, Wellcome

[2] Cambridge Biomedical Campus,MRC Cambridge Stem Cell Institute

[3] Present Address: European Molecular Biology Laboratory,Department of Artificial Intelligence, Computer Science School

[4] European Bioinformatics Institute,Department of Physiology and Biophysics

[5] EMBL-EBI,undefined

[6] Biotechnology and Planetary Protection Group,undefined

[7] Jet Propulsion Laboratory,undefined

[8] California Institute of Technology,undefined

[9] Universidad Nacional de Educación a Distancia (UNED),undefined

[10] Weill Cornell Medicine,undefined

[11] The HRH Prince Alwaleed Bin Talal Bin Abdulaziz Alsaud Institute for Computational Biomedicine,undefined

[12] Weill Cornell Medicine,undefined

[13] The WorldQuant Initiative for Quantitative Prediction,undefined

[14] Weill Cornell Medicine,undefined

[15] The Feil Family Brain and Mind Research Institute,undefined

[16] Weill Cornell Medicine,undefined

[17] KBR,undefined

[18] Space Biosciences Division,undefined

[19] NASA Ames Research Center,undefined

[20] Stanley Center for Psychiatric Research,undefined

[21] Broad Institute of MIT and Harvard,undefined

来源：

Microbiome | / 10卷

关键词：

ISS; Metagenomics; Antibiotic resistance; Machine learning; Space Omics; Microbiome; Built-environment; Microbial Tracking-1; NGS;

D O I：

暂无

中图分类号：

学科分类号：

摘要：

引用

共 488 条

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