MultiHub Forum

I'm a computational biologist, and my lab is starting to explore how machine learning can be applied to our large-scale genomic datasets to identify novel biomarkers, but we're hitting a wall with the "black box" problem and the need for interpretable, biologically plausible models. We have the data and some Python skills, but we lack the deep ML expertise to choose the right architectures or validate our findings beyond standard accuracy metrics. For researchers in other fields like materials science or astrophysics who have successfully integrated ML into your discovery pipeline, what was your learning curve like, and how did you bridge the gap between domain expertise and data science? I'm particularly interested in practical advice on collaborating with ML specialists, selecting models that provide some level of explainability, and avoiding common pitfalls like overfitting on noisy experimental data.

From my experience bridging wet-lab biology and ML, start with a concrete task. Pick one measurable discovery goal—a biomarker predictor from a gene-feature matrix—and build a simple baseline model like logistic regression with L2. Before you touch fancy neural nets, get a clean dataset, clear train/val/test splits, and a minimal feature set informed by biology (pathways, gene sets). Then iteratively add complexity: a tree-based model with SHAP explanations, then a small neural network if you have real data. Keep a reproducible pipeline (preprocessing, feature normalization, cross-validation) and require the ML partner to provide model cards plus an explanation of failure cases.