Modelling Microbial Communities with Graph Neural Networks

Albane Ruaud, Cansu Sancaktar, Marco Bagatella, Christoph Ratzke, Georg Martius
Proceedings of the 41st International Conference on Machine Learning, PMLR 235:42742-42765, 2024.

Abstract

Understanding the interactions and interplay of microorganisms is a great challenge with many applications in medical and environmental settings. In this work, we model bacterial communities directly from their genomes using graph neural networks (GNNs). GNNs leverage the inductive bias induced by the set nature of bacteria, enforcing permutation invariance and granting combinatorial generalization. We propose to learn the dynamics implicitly by directly predicting community relative abundance profiles at steady state, thus escaping the need for growth curves. On two real-world datasets, we show for the first time generalization to unseen bacteria and different community structures. To investigate the prediction results more deeply, we create a simulation for flexible data generation and analyze effects of bacteria interaction strength, community size, and training data amount.

Cite this Paper

BibTeX
@InProceedings{pmlr-v235-ruaud24a, title = {Modelling Microbial Communities with Graph Neural Networks}, author = {Ruaud, Albane and Sancaktar, Cansu and Bagatella, Marco and Ratzke, Christoph and Martius, Georg}, booktitle = {Proceedings of the 41st International Conference on Machine Learning}, pages = {42742--42765}, year = {2024}, editor = {Salakhutdinov, Ruslan and Kolter, Zico and Heller, Katherine and Weller, Adrian and Oliver, Nuria and Scarlett, Jonathan and Berkenkamp, Felix}, volume = {235}, series = {Proceedings of Machine Learning Research}, month = {21--27 Jul}, publisher = {PMLR}, pdf = {https://raw.githubusercontent.com/mlresearch/v235/main/assets/ruaud24a/ruaud24a.pdf}, url = {https://proceedings.mlr.press/v235/ruaud24a.html}, abstract = {Understanding the interactions and interplay of microorganisms is a great challenge with many applications in medical and environmental settings. In this work, we model bacterial communities directly from their genomes using graph neural networks (GNNs). GNNs leverage the inductive bias induced by the set nature of bacteria, enforcing permutation invariance and granting combinatorial generalization. We propose to learn the dynamics implicitly by directly predicting community relative abundance profiles at steady state, thus escaping the need for growth curves. On two real-world datasets, we show for the first time generalization to unseen bacteria and different community structures. To investigate the prediction results more deeply, we create a simulation for flexible data generation and analyze effects of bacteria interaction strength, community size, and training data amount.} }
Endnote
%0 Conference Paper %T Modelling Microbial Communities with Graph Neural Networks %A Albane Ruaud %A Cansu Sancaktar %A Marco Bagatella %A Christoph Ratzke %A Georg Martius %B Proceedings of the 41st International Conference on Machine Learning %C Proceedings of Machine Learning Research %D 2024 %E Ruslan Salakhutdinov %E Zico Kolter %E Katherine Heller %E Adrian Weller %E Nuria Oliver %E Jonathan Scarlett %E Felix Berkenkamp %F pmlr-v235-ruaud24a %I PMLR %P 42742--42765 %U https://proceedings.mlr.press/v235/ruaud24a.html %V 235 %X Understanding the interactions and interplay of microorganisms is a great challenge with many applications in medical and environmental settings. In this work, we model bacterial communities directly from their genomes using graph neural networks (GNNs). GNNs leverage the inductive bias induced by the set nature of bacteria, enforcing permutation invariance and granting combinatorial generalization. We propose to learn the dynamics implicitly by directly predicting community relative abundance profiles at steady state, thus escaping the need for growth curves. On two real-world datasets, we show for the first time generalization to unseen bacteria and different community structures. To investigate the prediction results more deeply, we create a simulation for flexible data generation and analyze effects of bacteria interaction strength, community size, and training data amount.
APA
Ruaud, A., Sancaktar, C., Bagatella, M., Ratzke, C. & Martius, G.. (2024). Modelling Microbial Communities with Graph Neural Networks. Proceedings of the 41st International Conference on Machine Learning, in Proceedings of Machine Learning Research 235:42742-42765 Available from https://proceedings.mlr.press/v235/ruaud24a.html.

Related Material