adverSCarial: assessing the vulnerability of single-cell RNA-sequencing classifiers to adversarial attacks.
Journal:
Bioinformatics (Oxford, England)
PMID:
40234247
Abstract
MOTIVATION: Several machine learning (ML) algorithms dedicated to the detection of healthy and diseased cell types from single-cell RNA sequencing (scRNA-seq) data have been proposed for biomedical purposes. This raises concerns about their vulnerability to adversarial attacks, exploiting threats causing malicious alterations of the classifiers' output with defective and well-crafted input.