Special Issue "Graph Neural Networks in Cancer Research"
A special issue of Cancers (ISSN 2072-6694). This special issue belongs to the section "Methods and Technologies Development".
Deadline for manuscript submissions: 31 December 2023 | Viewed by 261
Special Issue Editors
Interests: neuropathology; microglia in glioma
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
Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.
Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Cancers is an international peer-reviewed open access semimonthly journal published by MDPI.
Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.
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
