Sparsity is all you need: rethinking biologically informed neural networks.

Biologically informed neural networks are increasingly adopted in bioinformatics under the premise that embedding biological knowledge into model architectures yields more accurate and interpretable predictions. This approach has driven a growing literature of pathway-informed models aiming to move beyond black-box learning by explicitly encoding biological structure. However, it remains unclear whether these models exploit biological knowledge or instead benefit from a different inductive bias. Here, we systematically investigate this question across 29 state-of-the-art pathway-informed neural networks by explicitly decoupling biological annotations from network architecture. For each evaluable model, we implement a structure-matched randomization protocol, in which pathway annotations are replaced with random associations while preserving sparsity and architectural constraints, allowing for a direct comparison under controlled conditions. Across multiple prediction tasks, datasets, and evaluation metrics, the randomized models consistently match or outperform their biologically informed counterparts. Moreover, pathway-informed models show no systematic advantage in interpretability: randomized models recover disease-associated biomarkers with comparable accuracy and yield highly correlated feature rankings. Our results reveal that the performance gains commonly attributed to biological pathway integration arise predominantly from sparsity-induced regularization rather than from biological knowledge itself. We provide a general evaluation workflow to test whether biological priors contribute predictive information beyond sparsity, offering practical guidance for the development of biology-aware neural networks. The code implementing the proposed methodology is available on GitHub at https://github.com/compbiomed-unito/Pathway_Randomization.