by A groundbreaking new artificial intelligence (AI) algorithm, developed by a team of researchers at Penn State College of Medicine, is set to revolutionize the diagnosis and treatment of autoimmune diseases. Autoimmune diseases occur when the immune system mistakenly attacks the body’s own healthy cells and tissues.
The innovative AI algorithm delves deeper into the genetic code of these conditions, enabling more precise modeling of gene expression and regulation associated with specific Autoimmune Diseases. This advanced approach has the potential to identify previously unknown genes linked to these diseases, providing earlier and more accurate predictions.
The team’s research, published in Nature Communications, outperforms existing methodologies and uncovered an additional 26% of novel gene and trait associations. Dajiang Liu, Distinguished Professor, Vice Chair for Research, and Director of Artificial Intelligence and Biomedical Informatics at the Penn State College of Medicine, explained, “Understanding how any one of the DNA mutations we all carry may influence gene expression linked to disease is crucial for predicting disease risk early, especially for autoimmune diseases.”
Genetics play a significant role in disease development. Variations in DNA can impact gene expression, which in turn influences disease risk. However, traditional genome-wide association studies (GWAS) can only pinpoint regions of the genome associated with a particular disease or trait but cannot identify the specific genes responsible for disease risks.
Existing methods also lack the necessary granularity to analyze gene expression at the cellular level. Gene expression can be specific to certain cell types, and failing to distinguish between distinct cell types may overlook genuine causal relationships between genetic variants and gene expression.
To address these limitations, the research team introduced EXPRESSO (Expression PREdiction with Summary Statistics Only), an advanced AI algorithm that analyzes single-cell expression quantitative trait loci data, which links genetic variants to the genes they regulate. Additionally, EXPRESSO integrates 3D genomic data and epigenetics into its modeling, providing a more comprehensive understanding of the disease mechanisms.
The team applied EXPRESSO to GWAS datasets for 14 autoimmune diseases, including lupus, Crohn’s disease, ulcerative colitis, and rheumatoid arthritis. The results revealed many more risk genes for autoimmune diseases that have cell-type-specific effects, meaning they only impact gene expression in specific cell types.
Bibo Jiang, Assistant Professor at the Penn State College of Medicine and senior author of the study, stated, “These findings provide valuable insights into potential therapeutics for autoimmune diseases. Currently, there are limited long-term treatment options, and most treatments only mitigate symptoms. However, genomics and AI offer a promising route to develop novel therapeutics for these conditions.”
Laura Carrel, Professor of Biochemistry and Molecular Biology at the Penn State College of Medicine and co-senior author of the study, added, “The potential of this new AI algorithm to identify hidden genetic markers and develop targeted therapies for autoimmune diseases is truly exciting. It represents a significant step forward in our understanding and treatment of these complex conditions.”
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1. Source: Coherent Market Insights, Public sources, Desk research.
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