Back to search

Article

Unsupervised machine learning reveals key immune cell subsets in COVID-19, rhinovirus infection, and cancer therapy

2020-08-01

Abstract excerpt

For an emerging disease like COVID-19, systems immunology tools may quickly identify and quantitatively characterize cells associated with disease progression or clinical response. With repeated sampling, immune monitoring creates a real-time portrait of the cells reacting to a novel virus before disease specific knowledge and tools are established. However, single cell analysis tools can struggle to reveal rare c...

Topics

Open a Topic to create a Post that cites this publication.

Identifiers and source

Literature Corpus work
d60b2c34-d8af-5e75-b58b-7faa85d52da8
DOI
10.1101/2020.07.31.190454
Open publication

Related research

Semantic proximity does not establish scientific evidence.

Click a neighbor to travelStep 1 · 12 closest
Interactive article relationship graphSelect a related publication card to move it into the centre and load its closest explainable connections. Solid lines are source-backed structured connections. Dashed lines are semantic discovery signals and are not scientific evidence.
Unsupervised machine learning reveals key immune cell subsets in COVID-19, rhinovirus infection, and cancer therapyDOI 10.1101/2020.07.31.190454
Select a neighboring publication to make it the new centre.