AI Reveals Crucial Gene Sets Driving Complex Diseases
Researchers have developed an AI-powered method to identify key gene combinations that contribute to complex diseases, paving the way for personalized treatment approaches.
Scientists at Northwestern University have developed an innovative computational tool that uses artificial intelligence to identify key gene combinations responsible for complex illnesses such as diabetes, cancer, and asthma. Unlike traditional approaches that focus on single genes, this new method recognizes the intricate network of multiple genes that work together to influence disease outcomes. The challenge has always been the vast number of possible gene interactions, making it difficult to determine which gene sets contribute to disease development.
Utilizing a generative AI model, the approach amplifies limited gene expression data, allowing researchers to uncover patterns linked to disease traits. This enables the identification of gene groups that collectively impact the emergence of complex traits, which could lead to more targeted and effective treatments. The model, named TWAVE (Transcriptome-Wide conditional Variational auto-Encoder), emulates disease and health states, correlating gene expression shifts with phenotypic changes. It also considers environmental factors that influence gene activity but are not directly encoded in DNA.
The study, published in the Proceedings of the National Academy of Sciences, highlights the potential for personalized medicine by recognizing that different individuals may have distinct gene sets driving their diseases. This understanding could pave the way for customized treatments tailored to each person's unique genetic and environmental background.
Current methods like genome-wide association studies often fall short because they focus on individual genes rather than their interactions. By focusing on gene expression, the new tool bypasses privacy concerns related to genetic data and accounts for environmental influences that affect gene activity. Overall, this research offers a promising avenue for better understanding the complexity of human diseases and developing precise therapeutic strategies.
Source: https://medicalxpress.com/news/2025-06-ai-key-gene-complex-diseases.html
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