Simulation and empirical evaluation of biologically-informed neural network performance.

ABSTRACT: Biologically-informed neural networks (BiNNs) offer interpretable deep learning models for biological data, but the dataset characteristics required for strong performance remain poorly understood. For instance, we previously developed P-NET, a BiNN with an architecture based on the Reactome pathway database, and applied this model to predict metastatic status of patients with prostate cancer using somatic mutation and copy number information. It seems likely that including additional relevant signal - e.g., germline variation in this context - should improve model performance, but we currently lack a principled approach to assess whether BiNNs will successfully detect this signal. Here, we developed two simulation frameworks to evaluate the factors that influence BiNN performance - includ

SUBMITTER: Miller GA 

PROVIDER: S-EPMC12642320 | biostudies-literature | 2025 Nov

REPOSITORIES: biostudies-literature