Graph Neural Networks in Cancer Research
A special issue of Cancers (ISSN 2072-6694). This special issue belongs to the section "Cancer Informatics and Big Data".
Deadline for manuscript submissions: closed (31 December 2024) | Viewed by 3690
Special Issue Editors
Interests: neuropathology; microglia in glioma
Special Issues, Collections and Topics in MDPI journals
Interests: image processing and analysis; image registration for biomedical and multimedia applications; saliency detection, identification; clustering, segmentation and visual analytics of medical and health data
Special Issue Information
Dear Colleagues,
The advent of deep learning methods has enriched computer-aided research. Graph neural networks (GNNs) are already showing particular potential in biomedicine. Cancer research is at the forefront of this development, as examples from the fields of automatic detection and segmentation of tumors, prognostication, and anticancer drug design show. Significantly improved software frameworks and increasing computing power have contributed to this progress. GNNs are attracting particular attention due to their wide applicability, visual nature and interpretable decision-making ability. Through expanding conventional neural networks to non-Euclidean data, GNNs enable AI to learn geometric patterns from graph-structured representations and to provide insight into local and global relationships between entities. Notable developments include the application of relation–information theory to cancer identification, classification, segmentation and tracking for the optimization of personalized treatment, and the investigation of gene sequences and tumor heterogeneity.
For this Special Issue, we welcome original research articles or comprehensive review articles focusing on GNN-based methods in cancer research. We hope that such a collection will promote the development of GNN-based methods and provide novel tools for the fight against cancer.
Prof. Dr. Manuel B. Graeber
Dr. Xiuying Wang
Guest Editors
Manuscript Submission Information
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Keywords
- graph representation learning for cancer classification/grading
- interpretable/explainable graph neural network for cancer prognosis and survival prediction
- GNN-based cancer diagnosis and analysis strategies
- GNN-based drug discovery for cancer treatment
- graph-structured algorithm for cancer gene discovery and analysis
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