Employing Graph Representations for Cell-Level Characterization of Melanoma MELC Samples-Reference-Cited by-同舟云学术

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Employing Graph Representations for Cell-Level Characterization of Melanoma MELC Samples

Published:2023-04-18 Issue: Volume: Page:
ISSN:
Container-title:2023 IEEE 20th International Symposium on Biomedical Imaging (ISBI)
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Author:

Rivera Monroy Luis Carlos¹,Rist Leonhard¹,Eberhardt Martin²,Ostalecki Christian²,Baur Andreas²,Vera Julio²,Breininger Katharina³,Maier Andreas¹

Affiliation:

1. Friedrich-Alexander-Universität Erlangen-Nürnberg,Pattern Recognition Lab,Erlangen,Germany

2. Universitätsklinikum Erlangen,Department of Dermatology,Erlangen,Germany

3. FAU Erlangen-Nürnberg,Department Artificial Intelligence in Biomedical Engineering,Erlangen,Germany

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/10230311/10230322/10230519.pdf?arnumber=10230519

Reference24 articles.

1. Systematic High-Content Proteomic Analysis Reveals Substantial Immunologic Changes in Colorectal Cancer

2. Scikit-learn: Machine learning in Python;pedregosa;Journal of Machine Learning Research,2011

3. In-situ-topoproteome analysis of cutaneous lymphomas: Perspectives of assistance for dermatohistologic diagnostics by Multi Epitope Ligand Cartography (MELC)

4. Graph random neural networks for semi-supervised learning on graphs;feng;Advances in neural information processing systems,2020

5. SCANPY: large-scale single-cell gene expression data analysis

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