Back to search

Article

An Interpretable Deep Learning Model for Predicting the Risk of Severe COVID-19 from Spike Protein Sequence

2022-01-18

Abstract excerpt

Throughout the COVID-19 pandemic, the virus has mutated in ways that affect its ability to infect people, cause severe disease, and escape immunity. It can be costly and time-consuming to experimentally study viral mutations. Sequencing genetic code is cheaper, and millions of SARS-CoV-2 genome sequences are available. With the quickly changing dynamics of SARS-CoV-2 evolution and patient outcomes, we need fast wa...

Topics

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

Identifiers and source

Literature Corpus work
336034d1-278a-5e26-88ca-abe0dfd01cd9
DOI
10.21203/rs.3.rs-1234007/v1
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.
An Interpretable Deep Learning Model for Predicting the Risk of Severe COVID-19 from Spike Protein SequenceDOI 10.21203/rs.3.rs-1234007/v1
Select a neighboring publication to make it the new centre.