Northwestern University biophysicists unveil an AI-driven tool that identifies gene sets responsible for complex diseases like cancer and diabetes, paving the way for personalized treatments.
In a promising development for the future of medical research, biophysicists at Northwestern University have created an innovative computational tool that uncovers gene combinations responsible for complex diseases such as diabetes, cancer and asthma.
This breakthrough, made possible by a generative artificial intelligence (AI) model known as the Transcriptome-Wide conditional Variational auto-Encoder (TWAVE), could lead to groundbreaking advancements in medical treatments.
Complex diseases differ from single-gene disorders because they result from networks of multiple genes working together, making it exceptionally challenging to isolate the specific gene sets at play.
Current methodologies, such as genome-wide association studies, primarily focus on individual genes and therefore lack the capability to grasp the collective influence of multiple genes.
“Many diseases are determined by a combination of genes — not just one,” senior author Adilson Motter, the Charles E. and Emma H. Morrison Professor of Physics at Northwestern’s Weinberg College of Arts and Sciences, said in a news release. “You can compare a disease like cancer to an airplane crash. In most cases, multiple failures need to occur for a plane to crash, and different combinations of failures can lead to similar outcomes. This complicates the task of pinpointing the causes. Our model helps simplify things by identifying the key players and their collective influence.”
The study, set to be published in the Proceedings of the National Academy of Sciences, reveals that the AI model can amplify sparse gene expression data to resolve patterns of gene activity critical to complex traits.
This information may be pivotal in introducing new, more effective treatments based on gene networks rather than single genes.
“We’re not looking at gene sequences but gene expression,” added co-author Thomas Wytock, a research associate in Motter’s Lab. “We trained our model on data from clinical trials, so we know which expression profiles are healthy or diseased. For a smaller number of genes, we also have experimental data that tells how the network responds when the gene is turned on or off, which we can match with the expression data to find the genes implicated in the disease.”
Focusing on gene expression holds significant advantages. Unlike DNA sequences, expression data is a dynamic snapshot of cellular activity, which accounts for environmental factors affecting gene regulation.
This approach not only circumvents privacy issues tied to genetic data but also offers a more comprehensive understanding of how external factors influence gene activity.
The insights gained from TWAVE’s capabilities could also fuel the development of personalized treatments, as the data suggests that the same disease can stem from different gene sets in different individuals. This opens the door to tailoring medical interventions based on a patient’s unique genetic and environmental makeup.
“A disease can manifest similarly in two different individuals,” Motter added. “But, in principle, there could be a different set of genes involved for each person owing to genetic, environmental and lifestyle differences. This information could orient personalized treatment.”
The potential of TWAVE lies not only in its ability to identify gene sets responsible for complex diseases but also in paving the way for novel therapeutic strategies that target genetic networks, ultimately transforming how we approach the diagnosis and treatment of multifaceted health conditions.
Source: Northwestern University