PHOTONAI-Graph - a Python toolbox for graph machine learning

  • Graph data is an omnipresent way to represent information in machine learning. Especially, in neuroscience research, data from Diffusion-Tensor Imaging (DTI) and functional Magnetic Resonance Imaging (fMRI) is commonly represented as graphs. Exploiting the graph structure of these modalities using graph-specific machine learning applications is currently hampered by the lack of easy-to-use software. PHOTONAI Graph aims to close the gap between domain experts of machine learning, graph experts and neuroscientists. Leveraging the rapid machine learning model development features of the Python machine learning API PHOTONAI, PHOTONAI Graph enables the design, optimization, and evaluation of reliable graph machine learning models for practitioners. As such, it provides easy access to custom graph machine learning pipelines including, hyperparameter optimization and algorithm evaluation ensuring reproducibility and valid performance estimates. Integrating established algorithms such as graph neural networks, graph embeddings and graph kernels, it allows researchers without significant coding experience to build and optimize complex graph machine learning models within a few lines of code. We showcase the versatility of this toolbox by building pipelines for both resting–state fMRI and DTI data in the hope that it will increase the adoption of graph-specific machine learning algorithms in neuroscience research.

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Author:Jan ErnstingORCiDGND, Vincent Leonard HolsteinORCiD, Nils Ralf WinterORCiD, Kelvin SarinkORCiD, Ramona LeeningsORCiDGND, Marius GruberORCiD, Jonathan ReppleORCiDGND, Benjamin RisseORCiDGND, Udo DannlowskiORCiDGND, Tim HahnORCiDGND
Parent Title (English):medRxiv
Document Type:Preprint
Date of Publication (online):2023/06/29
Date of first Publication:2023/06/29
Publishing Institution:Universitätsbibliothek Johann Christian Senckenberg
Release Date:2023/09/05
Tag:Auto-ML; Graph Machine Learning; Graph Neural Networks; Network Neuroscience
Page Number:10
Institutes:Medizin / Medizin
Dewey Decimal Classification:0 Informatik, Informationswissenschaft, allgemeine Werke / 00 Informatik, Wissen, Systeme / 004 Datenverarbeitung; Informatik
Licence (German):License LogoCreative Commons - CC BY-NC-ND - Namensnennung - Nicht kommerziell - Keine Bearbeitungen 4.0 International