2024-11-17-zhang24a.md

abstract	title	layout	series	publisher	issn	id	month	tex_title	firstpage	lastpage	page	order	cycles	bibtex_author	author	date	address	container-title	volume	genre	issued	pdf	extras
Network inference, the task of reconstructing interactions in a complex system from experimental observables, is a central yet extremely challenging problem in systems biology. While much progress has been made in the last two decades, network inference remains an open problem. For systems observed at steady state, limited insights are available since temporal information is unavailable and thus causal information is lost. Two common avenues for gaining causal insights into system behaviour are to leverage temporal dynamics in the form of trajectories, and to apply interventions such as knock-out perturbations. We propose an approach for leveraging both dynamical and perturbational single cell data to jointly learn cellular trajectories and power network inference. Our approach is motivated by min-entropy estimation for stochastic dynamics and can infer directed and signed networks from time-stamped single cell snapshots.	Joint trajectory and network inference via reference fitting	inproceedings	Proceedings of Machine Learning Research	PMLR	2640-3498	zhang24a	0	Joint trajectory and network inference via reference fitting	72	85	72-85	72	false	Zhang, Stephen Y	given family Stephen Y Zhang	2024-11-17		Proceedings of the 19th Machine Learning in Computational Biology meeting	261	inproceedings	date-parts 2024 11 17	https://raw.githubusercontent.com/mlresearch/v261/main/assets/zhang24a/zhang24a.pdf