STIGMA: Single-cell tissue-specific gene prioritization using machine learning

Single-cell tissue-specific gene prioritization using machine learning (STIGMA) is an approach to prioritize candidate genes for congenital diseases. STIGMA uses single-cell RNA-seq data to capture the dynamics of gene expression within cell populations across developmental time, making it a powerful tool for the discovery of disease-associated genes.

Source: The American Journal of Human Genetics - January 15, 2024 Category: Genetics & Stem Cells Authors: Saranya Balachandran, Cesar A. Prada-Medina, Martin A. Mensah, Naseebullah Kakar, Inga Nagel, Jelena Pozojevic, Enrique Audain, Marc-Phillip Hitz, Martin Kircher, Varun K.A. Sreenivasan, Malte Spielmann Tags: Article Source Type: research