Back to search

Article

Predicting hotspots for disease-causing single nucleotide variants using sequences-based coevolution, network analysis, and machine learning

2023-08-31

Abstract excerpt

To enable personalized genetics and medicine, it is important yet highly challenging to accurately predict disease-causing mutations from the sequences alone at high throughput. To meet this challenge, we build upon recent progress in machine learning, network analysis, and protein language models, and develop a sequences-based variant site prediction workflow based on the protein residue contact networks: 1. We e...

Topics

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

Identifiers and source

Literature Corpus work
6ac91285-1775-53fb-9470-9465a1214386
DOI
10.22541/au.169346898.82764805/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.
Predicting hotspots for disease-causing single nucleotide variants using sequences-based coevolution, network analysis, and machine learningDOI 10.22541/au.169346898.82764805/v1
Select a neighboring publication to make it the new centre.