Join us for this seminar by Dr Pablo Rodríguez Mier (Heidelberg, Germany) as part of the North West Seminar Series in Mathematical Biology and Data Sciences. More details about the joint series can be found here https://northwestseminars.great-site.net/ .
The talk will be hosted via zoom, please contact carl.whitfield@manchester.ac.uk or igor.chernyavsky@manchester.ac.uk for the zoom link, or sign up to the mailing list.
Title: CORNETO: A unified framework for joint network inference from prior knowledge and omics data
Abstract:
Understanding biological systems relies on extracting interpretable insights from omics data, for which network models offer a natural representation of molecular interactions. Constructing context-specific networks for particular conditions is crucial for many biological analyses. However, current methods often present a trade-off between predictive power (machine learning approaches requiring large datasets and lacking interpretability) and interpretability (traditional knowledge-based methods that are fragmented and difficult to generalise across samples).
This work introduces CORNETO, a unified mathematical framework that reformulates diverse network inference problems as mixed integer optimisation problems leveraging network flows and structured sparsity. This enables the joint inference of biological networks across multiple samples, enhancing the discovery of both shared and sample-specific molecular mechanisms whilst yielding sparser, more interpretable results. Importantly, many existing network inference methods are shown to be special cases within this unified framework.
CORNETO accommodates various prior knowledge structures, including undirected, directed, signed, and hypergraphs, and generalises a broad class of approaches, from Steiner trees to flux balance analysis, within a single optimisation-based interface. The utility of the framework is demonstrated across diverse biological contexts, including signalling, metabolism, and machine learning integration.
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