Source: Thinkstock April 19, 2024 – Researchers from Clemson University have developed a deep learning tool to better understand how gene-regulatory network (GRN) interactions impact individual drug response, according to a study published recently in Nature Biotechnology .
GRNs “map the complex interactions between genes, regulatory elements and proteins, holding the key to understanding how genetic variations influence [phenotypes] like drug response,” explained Zhana Duren, PhD, an assistant professor in the Department of Genetics and Biochemistry at Clemson, in a news release . “Each individual possesses a unique GRN shaped by their specific genotype, explaining why the same drug can elicit different responses in different people.”
The research team further indicated that the majority of known genetic variants linked to particular diseases lie in parts of a person’s deoxyribonucleic acid (DNA) that do not directly code for proteins, making it difficult to determine the role these variants play in individual health.
To help shed light on these genetic variants and their GRNs, the researchers developed a deep learning tool known as Lifelong Neural Network for Gene Regulation (LINGER).
“[We] aim to answer critical questions, (such as) how and why do genetic variants influence individual phenotypes through intricate GRN interactions,” Duren stated. “By elucidating these mechanisms, we pave the way for predicting drug response based on personal genetics, enabling the development of more-targeted therapies and minimizing ineffective treatments.”
LINGER is designed to infer GRNs from single-cell multiome data.
The researchers underscored that there are existing models designed to predict how GRNs work, but these are limited […]
Deep learning tool may advance precision medicine approaches
