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In Elmarakeby et al., "Biologically informed deep neural network for prostatecancer 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 moreup-to-date libraries. We quantified the contribution of network sparsificationby Reactome biological pathways, and confirmed its importance to P-NET'ssuperior performance. Furthermore, we explored alternative neural architecturesand approaches to incorporating biological information into the networks. Weexperimented with three types of graph neural networks on the same trainingdata, and investigated the clinical prediction agreement between differentmodels. Our analyses demonstrated that deep neural networks with distinctarchitectures make incorrect predictions for individual patient that arepersistent across different initializations of a specific neural architecture.This suggests that different neural architectures are sensitive to differentaspects of the data, an important yet under-explored challenge for clinicalprediction tasks.

Reusability report: Prostate cancer stratification with diverse biologically-informed neural architectures