2000 character limit reached
Comparative Analysis of Radiomic Features and Gene Expression Profiles in Histopathology Data Using Graph Neural Networks
Published 25 Dec 2023 in cs.CV, cs.CE, and cs.LG | (2312.15825v1)
Abstract: This study leverages graph neural networks to integrate MELC data with Radiomic-extracted features for melanoma classification, focusing on cell-wise analysis. It assesses the effectiveness of gene expression profiles and Radiomic features, revealing that Radiomic features, particularly when combined with UMAP for dimensionality reduction, significantly enhance classification performance. Notably, using Radiomics contributes to increased diagnostic accuracy and computational efficiency, as it allows for the extraction of critical data from fewer stains, thereby reducing operational costs. This methodology marks an advancement in computational dermatology for melanoma cell classification, setting the stage for future research and potential developments.
- Walter Schubert “Topological Proteomics, Toponomics, MELK-Technology” In Proteomics of Microorganisms: Fundamental Aspects and Application Berlin, Heidelberg: Springer Berlin Heidelberg, 2003, pp. 189–209 DOI: 10.1007/3-540-36459-5_8
- “Quantitative Fluorescence Resonance Energy Transfer Analysis on the Direct Interaction of Activation-2b with Histone H3/Switch-3B Protein in Arabidopsis Mesophyll Protoplasts” In J Fluoresc Springer, 2021, pp. 981–988
- “In situ localization of epidermal stem cells using a novel multi epitope ligand cartography approach” In Integrative Biology 2.5-6, 2010, pp. 241–249 DOI: 10.1039/b926147h
- “The CD11a Binding Site of Efalizumab in Psoriatic Skin Tissue as Analyzed by Multi-Epitope Ligand Cartography Robot Technology: Introduction of a Novel Biological Drug-Binding Biochip Assay” In Skin Pharmacol Physiol 20.2, 2006, pp. 96–111 DOI: 10.1159/000097982
- “Employing Graph Representations for Cell-Level Characterization of Melanoma MELC Samples” In 2023 IEEE 20th International Symposium on Biomedical Imaging (ISBI), 2023, pp. 1–5 DOI: 10.1109/ISBI53787.2023.10230519
- “Single-cell landscape of bone marrow metastases in human neuroblastoma unraveled by deep multiplex imaging” In bioRxiv Cold Spring Harbor Laboratory, 2020, pp. 2020–09
- “A machine learning–based multidimensional model integrating clinical, radiomics, and cell-free DNA methylation biomarkers for the classification of pulmonary nodules.” In Journal of Clinical Oncology 41.16_suppl, 2023, pp. 3070–3070 DOI: 10.1200/JCO.2023.41.16\_suppl.3070
- “Learning deep features for dead and living breast cancer cell classification without staining” In Sci Rep 11.1 Nature Publishing Group UK London, 2021, pp. 10304
- “Analysis of cross-combinations of feature selection and machine-learning classification methods based on [18F] F-FDG PET/CT radiomic features for metabolic response prediction of metastatic breast cancer lesions” In Cancers 14.12 MDPI, 2022, pp. 2922
- “Prostate Gleason Score Detection by Calibrated Machine Learning Classification through Radiomic Features” In Applied Sciences 12.23 MDPI, 2022, pp. 11900
- Asmita Chopra, Rohit Sharma and Uma NM Rao “Pathology of melanoma” In Surgical Clinics 100.1 Elsevier, 2020, pp. 43–59
- “Computational models of melanoma” In Theoretical Biology and Medical Modelling 17.1 BioMed Central, 2020, pp. 1–16
- “Cellpose 2.0: how to train your own model” In Nat Methods 19.12 Nature Publishing Group US New York, 2022, pp. 1634–1641
- F Alexander Wolf, Philipp Angerer and Fabian J Theis “SCANPY: large-scale single-cell gene expression data analysis” In Genome Biol 19 Springer, 2018, pp. 1–5
- “Squidpy: a scalable framework for spatial omics analysis” In Nat Methods 19.2 Nature Publishing Group US New York, 2022, pp. 171–178
- “Computational Radiomics System to Decode the Radiographic Phenotype” In Cancer Res 77.21, 2017, pp. e104–e107 DOI: 10.1158/0008-5472.CAN-17-0339
- “Local Optimization of MAPF solutions on Directed Graphs”, 2023 arXiv:2304.01765 [math.OC]
- Hai-Yun Wang, Jian-ping Zhao and Chun-Hou Zheng “SUSCC: secondary construction of feature space based on UMAP for rapid and accurate clustering large-scale single cell RNA-seq data” In Interdisciplinary Sciences: Computational Life Sciences 13 Springer, 2021, pp. 83–90
- Van Hoan Do and Stefan Canzar “A generalization of t-SNE and UMAP to single-cell multimodal omics” In Genome Biol 22.1 BioMed Central, 2021, pp. 1–9
- “Hact-net: A hierarchical cell-to-tissue graph neural network for histopathological image classification” In Uncertainty for Safe Utilization of Machine Learning in Medical Imaging, and Graphs in Biomedical Image Analysis, 2020, pp. 208–219 Springer
- “Graph random neural networks for semi-supervised learning on graphs” In Adv Neural Inf Process Syst 33, 2020, pp. 22092–22103
Paper Prompts
Sign up for free to create and run prompts on this paper using GPT-5.
Top Community Prompts
Collections
Sign up for free to add this paper to one or more collections.