A hidden cause of the reproducibility crisis is how researchers select models like cell lines or mice. The choice is often driven by convenience—what a neighboring lab has available—rather than a systematic evaluation of which model is best suited to answer the specific scientific question.
To ensure patients get the same result from any test provider, the field must standardize not just the underlying sequencing technology, but also the software pipelines for data analysis and the clinical frameworks for interpreting results. Each layer presents a unique harmonization challenge.
The ultimate goal of precision medicine is a unique drug for each patient. However, this N-of-1 model directly conflicts with the current economic and regulatory system, which incentivizes developing drugs for large populations to recoup massive R&D and approval costs.
A lack of representation in genomic data has direct clinical consequences. A deep understanding of European genetics and a poor understanding of other groups has already manifested in less precise medical treatments for non-European populations, undermining the core promise of precision medicine.
Nonprofits occupy a unique space. While academia pursues discovery and industry seeks revenue, nonprofits can fund "infrastructure" projects like large, open-access datasets. These efforts accelerate the entire ecosystem, a goal neither academia nor industry is incentivized to pursue alone.
To truly understand biological systems, data scale is less important than data quality. The most informative data comes from capturing the dynamic interactions of a system *while* it's being perturbed (e.g., by a drug), not from static snapshots of a system at rest.
Applying AI to biology isn't just a big data problem. The training data must be structured for reinforcement learning. This means it must be complete (including negative results) and allow for a feedback loop where AI predictions are tested in the lab, and the results are used to refine the model.
When government funding for science is volatile, the biggest long-term risk is losing a generation of talent. Nonprofits can provide stability by funding postdoctoral fellows and junior faculty. This shores up the scientific foundation and prevents a loss of talent that can't be undone later.