A Graph Based Neural Network Approach to Immune Profiling of Multiplexed Tissue Samples
- Resource Type
- Conference
- Authors
- Martin, Natalia Garcia; Malacrino, Stefano; Wojciechowska, Marta; Campo, Leticia; Jones, Helen; Wedge, David C.; Holmes, Chris; Sirinukunwattana, Korsuk; Sailem, Heba; Verrill, Clare; Rittscher, Jens
- Source
- 2022 44th Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC) Engineering in Medicine & Biology Society (EMBC), 2022 44th Annual International Conference of the IEEE. :3063-3067 Jul, 2022
- Subject
- Bioengineering
Multiplexing
Proteins
Morphology
Predictive models
Graph neural networks
Topology
Convolutional neural networks
- Language
- ISSN
- 2694-0604
Multiplexed immunofluorescence provides an un-precedented opportunity for studying specific cell-to-cell and cell microenvironment interactions. We employ graph neural networks to combine features obtained from tissue morphology with measurements of protein expression to profile the tumour microenvironment associated with different tumour stages. Our framework presents a new approach to analysing and processing these complex multi-dimensional datasets that overcomes some of the key challenges in analysing these data and opens up the opportunity to abstract biologically meaningful interactions.