Studying gene expression in a cancer patient’s cells provides clinical biologists with crucial insights into the disease’s origin and helps predict how well various treatments might work. However, cells are highly complex, with multiple interconnected layers of biological information. The choice of measurement method significantly influences what data researchers can gather—and what conclusions they can draw. For example, analyzing protein levels within a cell can reveal different aspects of cancer biology compared to measuring gene expression or assessing cell shape and structure. Each measurement captures a unique piece of the puzzle: gene expression shows which genes are active, protein levels indicate functional activity, and morphological features reflect cellular health and behavior. Researchers now have a new ally in navigating this complexity. An AI-powered tool has been developed to help scientists determine which cell measurements are most informative in specific contexts. By analyzing vast datasets across different cell types and conditions, the tool identifies which features—whether genetic, proteomic, or structural—carry the most predictive power for outcomes like treatment response or disease progression. This capability allows researchers to move beyond relying on a single type of measurement and instead take a more holistic view. Instead of choosing between gene expression or morphology, they can use AI to understand how these layers interact and which combinations provide the clearest picture of a cell’s state. By integrating multiple data types and prioritizing the most relevant measurements, the tool helps biologists make more accurate, data-driven decisions—especially in the high-stakes context of cancer diagnosis and therapy. Ultimately, it enables a deeper, more comprehensive understanding of cellular behavior, bringing researchers closer to personalized and effective treatments.