The Strong Product Model for Network Inference without Independence Assumptions

Bailey Andrew, David Robert Westhead, Luisa Cutillo
Proceedings of The 28th International Conference on Artificial Intelligence and Statistics, PMLR 258:5230-5238, 2025.

Abstract

Multi-axis graphical modelling techniques allow us to perform network inference without making independence assumptions. This is done by replacing the independence assumption with a weaker assumption about the interaction between the axes; there are several choices for which assumption to use. In single-cell RNA sequencing data, genes may interact differently depending on whether they are expressed in the same cell, or in different cells. Unfortunately, current methods are not able to make this distinction. In this paper, we address this problem by introducing the strong product model for Gaussian graphical modelling.

Cite this Paper

BibTeX
@InProceedings{pmlr-v258-andrew25a, title = {The Strong Product Model for Network Inference without Independence Assumptions}, author = {Andrew, Bailey and Westhead, David Robert and Cutillo, Luisa}, booktitle = {Proceedings of The 28th International Conference on Artificial Intelligence and Statistics}, pages = {5230--5238}, year = {2025}, editor = {Li, Yingzhen and Mandt, Stephan and Agrawal, Shipra and Khan, Emtiyaz}, volume = {258}, series = {Proceedings of Machine Learning Research}, month = {03--05 May}, publisher = {PMLR}, pdf = {https://raw.githubusercontent.com/mlresearch/v258/main/assets/andrew25a/andrew25a.pdf}, url = {https://proceedings.mlr.press/v258/andrew25a.html}, abstract = {Multi-axis graphical modelling techniques allow us to perform network inference without making independence assumptions. This is done by replacing the independence assumption with a weaker assumption about the interaction between the axes; there are several choices for which assumption to use. In single-cell RNA sequencing data, genes may interact differently depending on whether they are expressed in the same cell, or in different cells. Unfortunately, current methods are not able to make this distinction. In this paper, we address this problem by introducing the strong product model for Gaussian graphical modelling.} }
Endnote
%0 Conference Paper %T The Strong Product Model for Network Inference without Independence Assumptions %A Bailey Andrew %A David Robert Westhead %A Luisa Cutillo %B Proceedings of The 28th International Conference on Artificial Intelligence and Statistics %C Proceedings of Machine Learning Research %D 2025 %E Yingzhen Li %E Stephan Mandt %E Shipra Agrawal %E Emtiyaz Khan %F pmlr-v258-andrew25a %I PMLR %P 5230--5238 %U https://proceedings.mlr.press/v258/andrew25a.html %V 258 %X Multi-axis graphical modelling techniques allow us to perform network inference without making independence assumptions. This is done by replacing the independence assumption with a weaker assumption about the interaction between the axes; there are several choices for which assumption to use. In single-cell RNA sequencing data, genes may interact differently depending on whether they are expressed in the same cell, or in different cells. Unfortunately, current methods are not able to make this distinction. In this paper, we address this problem by introducing the strong product model for Gaussian graphical modelling.
APA
Andrew, B., Westhead, D.R. & Cutillo, L.. (2025). The Strong Product Model for Network Inference without Independence Assumptions. Proceedings of The 28th International Conference on Artificial Intelligence and Statistics, in Proceedings of Machine Learning Research 258:5230-5238 Available from https://proceedings.mlr.press/v258/andrew25a.html.

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