Raum, H. | Search bioRxiv

bioRxiv2026

Evaluating Transferability and Robustness of Process-Guided Neural Networks in Forest Carbon Flux Modelling

Making robust and generalizable predictions within ecological systems such as forests remains challenging due to limited data availability and the slow pace of environmental change. To address this, we integrate a semi-empirical environmental process model (PRELES) to support deep learning approaches, specifically artificial neural networks (ANNs). We replicate and extend previous work on process-guided neural networks (PGNN) by introducing new model types and conducting a comprehensive hyperparameter optimisation within systematic nested cross-validation analyses in both data-thinning and extrapolative scenarios. Results show that both data-driven ANNs and PGNNs consistently outperform the stand-alone process model, while PGNNs provide additional advantages over ANNs in data-sparse settings and under transfer scenarios to unseen, changing climatic conditions. We further estimate the generalisation error for data-driven models as a function of the amount of training data, allowing for guidance on model suitability under different data availability. A variable importance analysis using accumulated local effects reveals that both PGNNs and ANNs learn simple, physically plausible relationships, whereas PRELES exhibits a strong bias toward boreal conditions and limited ability to predict unseen, climatically divergent sites. HighlightsO_LIProcess-guided, and plain neural networks outperform a calibrated process-based model (PRELES) in predicting forest ecosystem carbon fluxes. C_LIO_LIProcess-guided neural networks provide advantages over naive neural networks in sparse-data settings and show greater robustness under transferable scenarios with unseen changing climatic conditions. C_LIO_LIVariable-importance analyses using accumulated local effects show that both process-guided and naive neural networks learn simple yet physically plausible relationships between meteorological drivers and target responses, whereas the process model (PRELES) exhibits a better fit toward boreal conditions and limited ability to predict unseen, climatically divergent sites. C_LI

ecology↗

bioRxiv2024

Enhancement of network architecture alignment in comparative single-cell studies

1Animal data can provide meaningful context for human gene expression at the single-cell level. This context can improve cell-type or cell-state detection and clarify how well the animal models human biological processes. To achieve this, we propose a deep learning approach that identifies a unified latent space to map complex patterns between datasets. Specifically, we combine variational autoencoders with a data-level nearest neighbor search to align neural network architectures across species. We visualize commonalities by mapping cell samples into the latent space. The aligned latent representation facilitates information transfer in applications of liver, white adipose tissue, and glioblastoma cells from various animal models. We also identify genes that exhibit systematic differences and commonalities between species. The results are robust for small datasets and with large differences in the observed gene sets. Thus, we reliably uncover and exploit similarities between species to provide context for human single-cell data.

bioinformatics↗