Back to search

Article

Self-supervised deep learning uncovers the semantic landscape of drug-induced latent mitochondrial phenotypes

2023-09-14

Abstract excerpt

<h4>SUMMARY</h4> Imaging-based high-content screening aims to identify substances that modulate cellular phenotypes. Traditional approaches screen compounds for their ability to shift disease phenotypes toward healthy phenotypes, but these end point-based screens lack an atlas-like mapping between phenotype and cell state that covers the full spectrum of possible phenotypic responses. In this study, we present Mi...

Topics

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

Identifiers and source

Literature Corpus work
e832f653-5e82-561e-bfa3-ed530404f807
DOI
10.1101/2023.09.13.557636
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.
Self-supervised deep learning uncovers the semantic landscape of drug-induced latent mitochondrial phenotypesDOI 10.1101/2023.09.13.557636
Select a neighboring publication to make it the new centre.