Data

Host transcriptomics and machine learning for bacterial infections in patients with COVID-19: a prospective cohort study

The University of Queensland
Associate Professor Kirsty Short (Aggregated by) Associate Professor Kirsty Short (Aggregated by) Dr Meagan Carney (Aggregated by) Dr Meagan Carney (Aggregated by)
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Contact Information

m.carney@uq.edu.au

Full description

This contains the code used to perform the analysis and generate the tables/figures in the manuscript named. *Please note that this code will not run without the supporting and raw data files. Please contact the corresponding author for access to this data.

Issued: 2023

Data time period: 03 2020 to 31 10 2021

This dataset is part of a larger collection

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Subjects
Biological Sciences | Biological Sciences | Bioinformatics and Computational Biology | Genomics and Transcriptomics | Information and Computing Sciences | Microbiology | Machine Learning | Virology | eng |

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Other Information
Host transcriptomics and machine learning for secondary bacterial infections in patients with COVID-19: a prospective, observational cohort study

local : UQ:3523420

Carney, Meagan, Pelaia, Tiana Maria, Chew, Tracy, Teoh, Sally, Phu, Amy, Kim, Karan, Wang, Ya, Iredell, Jonathan, Zerbib, Yoann, McLean, Anthony, Schughart, Klaus, Tang, Benjamin, Shojaei, Maryam, Short, Kirsty R and PREDICT-19 consortium (2024). Host transcriptomics and machine learning for secondary bacterial infections in patients with COVID-19: a prospective, observational cohort study. The Lancet Microbe, 5 (3), e272-e281. doi: 10.1016/S2666-5247(23)00363-4

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local : UQ:289097

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