TY  - INPR
A1  - Ernsting, Jan
A1  - Holstein, Vincent Leonard
A1  - Winter, Nils Ralf
A1  - Sarink, Kelvin
A1  - Leenings, Ramona
A1  - Gruber, Marius
A1  - Repple, Jonathan
A1  - Risse, Benjamin
A1  - Dannlowski, Udo
A1  - Hahn, Tim
T1  - PHOTONAI-Graph - a Python toolbox for graph machine learning
T2  - medRxiv
N2  - 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.
KW  - Graph Machine Learning
KW  - Network Neuroscience
KW  - Graph Neural Networks
KW  - Auto-ML
Y1  - 2023
UR  - http://publikationen.ub.uni-frankfurt.de/frontdoor/index/index/docId/74468
UR  - https://nbn-resolving.org/urn:nbn:de:hebis:30:3-744686
IS  - 2023.06.22.23291748
ER  -