MultiHub Forum

I'm a postdoc in computational biology, and our lab is exploring how to integrate machine learning into our research pipeline for analyzing complex genomic datasets. While the potential is huge, we're cautious about applying black-box models where interpretability is crucial for scientific discovery. We need methods that provide robust predictions but also offer insights into biological mechanisms. For researchers who have successfully implemented ML in similar hypothesis-driven science, what frameworks or model types have you found most effective for maintaining a balance between predictive power and interpretability?