In, Elmarakeby et al., "Biologically informed deep neural network for prostate cancer discovery", a feedforward neural network with biologically informed, sparse connections (P-NET) was presented to model the state of prostate cancer. We verified the reproducibility of the study conducted by Elmarakeby et al., using both their original codebase, and our own re-implementation using more up-to-date libraries. We quantified the contribution of network sparsification by Reactome biological pathways, and confirmed its importance to P-NET's superior performance. Furthermore, we explored alternative neural architectures and approaches to incorporating biological information into the networks. We experimented with three types of graph neural networks on the same training data, and investigated the clinical prediction agreement between different models. Our analyses demonstrated that deep neural networks with distinct architectures make incorrect predictions for individual patient that are persistent across different initializations of a specific neural architecture. This suggests that different neural architectures are sensitive to different aspects of the data, an important yet under-explored challenge for clinical prediction tasks.

翻译：在Elmarakeby等人的研究《基于生物信息深度神经网络的前列腺癌发现》中，提出了一种具有生物信息稀疏连接的前馈神经网络（P-NET），用于建模前列腺癌状态。我们利用其原始代码库以及采用更新库自行重新实现的方式，验证了Elmarakeby等人研究结果的可复现性。我们量化了通过Reactome生物通路实现网络稀疏化的贡献，并确认了其对P-NET优越性能的重要性。此外，我们探索了替代性神经架构以及将生物信息融入网络的不同方法。我们在相同训练数据上实验了三种图神经网络，并研究了不同模型间临床预测的一致性。我们的分析表明，具有不同架构的深度神经网络对个体患者的错误预测在特定神经架构的不同初始化中具有持续性。这揭示了不同神经架构对数据不同方面的敏感性，这是临床预测任务中一个至关重要但尚未充分探索的挑战。