Core Insights - A new computational tool named TWAVE has been developed by a team from Northwestern University, utilizing generative AI to extract key information from limited gene expression data and identify multi-gene combinations behind complex diseases [1][2] - The TWAVE model simulates gene expression under healthy and diseased states, linking changes in gene activity to phenotypic variations, and accurately pinpointing key gene changes that may trigger cellular state transitions [1][2] Group 1: Technology and Methodology - TWAVE focuses on gene expression levels rather than gene sequences, addressing the limitations of traditional genome-wide association studies (GWAS) that primarily identify single genes associated with specific traits [2] - The model was trained using clinical trial data to recognize expression profiles representing healthy or diseased states, enhancing its ability to identify disease-associated gene networks [2] - TWAVE circumvents privacy issues related to gene sequences and inherently incorporates environmental factors, allowing for a more comprehensive understanding of gene-environment interactions [2] Group 2: Applications and Implications - Testing of TWAVE on various complex diseases demonstrated its capability to identify known pathogenic genes and discover new genes overlooked by existing methods [2] - The findings indicate that the same disease may arise from different gene combinations in different populations, providing a theoretical basis for personalized treatment based on individual genetic drivers [2][3] - The advancements in AI within the life sciences are facilitating a deeper understanding of disease mechanisms and supporting early diagnosis and personalized treatment, accelerating the arrival of the precision medicine era [3]