Biologically-informed neural networks typically leverage pathway annotations to enhance performance in biomedical applications. We hypothesized that the benefits of pathway integration does not arise from its biological relevance, but rather from the sparsity it introduces. We conducted a comprehensive analysis of all relevant pathway-based neural network models for predictive tasks, critically evaluating each study's contributions. From this review, we curated a subset of methods for which the source code was publicly available. The comparison of the biologically informed state-of-the-art deep learning models and their randomized counterparts showed that models based on randomized information performed equally well as biologically informed ones across different metrics and datasets. Notably, in 3 out of the 15 analyzed models, the randomized versions even outperformed their biologically informed counterparts. Moreover, pathway-informed models did not show any clear advantage in interpretability, as randomized models were still able to identify relevant disease biomarkers despite lacking explicit pathway information. Our findings suggest that pathway annotations may be too noisy or inadequately explored by current methods. Therefore, we propose a methodology that can be applied to different domains and can serve as a robust benchmark for systematically comparing novel pathway-informed models against their randomized counterparts. This approach enables researchers to rigorously determine whether observed performance improvements can be attributed to biological insights.

本研究解决了生物信息指导的神经网络在生物医学应用中面临的模型效果与生物相关性不足的问题。我们提出了一个新方法，证明稀疏性比生物通路的相关性更能提升模型性能，并发现随机化模型在多个指标和数据集上的表现与生物指导模型相当，甚至在某些情况下优于其生物指导对手。这一结果提示生物通路注释可能过于嘈杂，应采用我们的标准化方法来系统比较新模型与随机模型之间的差异。

稀疏性就是你所需的：重新思考生物通路指导的深度学习方法