Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
7.4
5.2
Journals
Cancers
Special Issues
Graph Neural Networks in Cancer Research
share announcement

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: 31 December 2024 | Viewed by 1754

Special Issue Editors

Prof. Dr. Manuel B. Graeber

E-Mail Website
Guest Editor
Ken Parker Brain Tumor Research Laboratories, Brain and Mind Center, Faculty of Medicine and Health, University of Sydney, Sydney, NSW 2050, Australia
Interests: neuropathology; microglia in glioma
Dr. Xiuying Wang

E-Mail Website
Guest Editor
School of Computer Science, The University of Sydney, Sydney, NSW 2006, Australia
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 2900 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

Published Papers (1 paper)

Order results
Result details
Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:

Research

17 pages, 5328 KiB  
Article
PathoGraph: An Attention-Based Graph Neural Network Capable of Prognostication Based on CD276 Labelling of Malignant Glioma Cells
by Islam Alzoubi, Lin Zhang, Yuqi Zheng, Christina Loh, Xiuying Wang and Manuel B. Graeber
Cancers 2024, 16(4), 750; https://doi.org/10.3390/cancers16040750 - 11 Feb 2024
Viewed by 953
Abstract
Computerized methods have been developed that allow quantitative morphological analyses of whole slide images (WSIs), e.g., of immunohistochemical stains. The latter are attractive because they can provide high-resolution data on the distribution of proteins in tissue. However, many immunohistochemical results are complex because [...] Read more.
Computerized methods have been developed that allow quantitative morphological analyses of whole slide images (WSIs), e.g., of immunohistochemical stains. The latter are attractive because they can provide high-resolution data on the distribution of proteins in tissue. However, many immunohistochemical results are complex because the protein of interest occurs in multiple locations (in different cells and also extracellularly). We have recently established an artificial intelligence framework, PathoFusion which utilises a bifocal convolutional neural network (BCNN) model for detecting and counting arbitrarily definable morphological structures. We have now complemented this model by adding an attention-based graph neural network (abGCN) for the advanced analysis and automated interpretation of such data. Classical convolutional neural network (CNN) models suffer from limitations when handling global information. In contrast, our abGCN is capable of creating a graph representation of cellular detail from entire WSIs. This abGCN method combines attention learning with visualisation techniques that pinpoint the location of informative cells and highlight cell–cell interactions. We have analysed cellular labelling for CD276, a protein of great interest in cancer immunology and a potential marker of malignant glioma cells/putative glioma stem cells (GSCs). We are especially interested in the relationship between CD276 expression and prognosis. The graphs permit predicting individual patient survival on the basis of GSC community features. Our experiments lay a foundation for the use of the BCNN-abGCN tool chain in automated diagnostic prognostication using immunohistochemically labelled histological slides, but the method is essentially generic and potentially a widely usable tool in medical research and AI based healthcare applications. Full article
(This article belongs to the Special Issue Graph Neural Networks in Cancer Research)
►▼ Show Figures

Figure 1

Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:
 
Displaying articles 1-1
Back to TopTop