High-dimensional biology, actionable targets: advancing drug discovery through transcriptomics
09 Sept 2026
Target Identification
- High-dimensional transcriptomic data can generate a long list of interesting signals. How do you distinguish a biologically meaningful, druggable node from a statistically interesting one?
- Where do you see the biggest gap today between disease biology and chemical space, and how can transcriptomics help close that gap in a practical drug discovery workflow?
- When transcriptomic data point to a pathway rather than a single target, how do you decide whether to pursue a specific node, a network state, or even a phenotypic strategy?
- How do you validate that a transcriptomic signature is not just correlated with disease, but actually reflects a causal mechanism that can be therapeutically modulated?
- Looking ahead, what will make the biggest difference in turning high-dimensional data into real medicines: better datasets, better models, better perturbation tools, or tighter integration with chemistry?
Industry Expert
