AI Tool Uncovers Gene Networks Behind Complex Diseases

Northwestern University biophysicists have introduced a computational method to identify combinations of genes involved in complex diseases such as diabetes, cancer, and asthma. Unlike single-gene disorders, these illnesses are influenced by networks of multiple genes, making it difficult to isolate the specific contributors. The new approach uses a generative artificial intelligence (AI) model to amplify limited gene expression data, helping researchers detect patterns of gene activity linked to complex traits. This could inform more effective treatments targeting multiple genes. 

The study, published in the Proceedings of the National Academy of Sciences, was led by Adilson Motter, who noted “Many diseases are determined by a combination of genes—not just one. 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.”

Traditional methods like genome-wide association studies often lack the power to detect the collective influence of gene groups. Motter explained that while humans have only about six times more genes than bacteria, it is the interactions among genes that account for biological complexity.

The new tool, called Transcriptome-Wide conditional Variational auto-Encoder (TWAVE), combines machine learning and optimization to analyze gene expression data. TWAVE identifies groups of genes responsible for a trait and uses optimization to pinpoint changes most likely to shift a cell from healthy to diseased. The model focuses on gene expression rather than DNA sequence, which avoids privacy concerns and captures environmental influences.

Testing TWAVE on several diseases, the team found it could identify disease-causing genes missed by other methods. It also showed that different gene sets can cause the same disease in different people. “A disease can manifest similarly in two different individuals,” Motter said. “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.”