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πŸ“„Disease Prediction Using Graph Machine Learning Based on Electronic Health Data: A Review of Approaches and Trends

πŸ“˜journal: HEALTHCARE-BASEL (I.F=2.8)
πŸ—“Publish year: 2023

πŸ“ŽStudy paper

πŸ“±Channel: @ComplexNetworkAnalysis
#paper #Disease #Prediction #Graph_Machine_Learning #Electronic #Health #Trends #Review
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πŸ“„Privacy-Preserving Graph Machine Learning from Data to Computation: A Survey

πŸ—“Publish year: 2023

πŸ“ŽStudy paper

πŸ“±Channel: @ComplexNetworkAnalysis
#paper #Privacy #Preserving #Graph_Machine_Learning #Computation #survey
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πŸ“ƒGraph Machine Learning in the Era of Large Language Models (LLMs)

πŸ—“ Publish year: 2023

πŸ§‘β€πŸ’»Authors: Wenqi Fan, Shijie Wang, Jiani Huang, Zhikai Chen, Yu Song, Wenzhuo Tang, Haitao Mao, Hui Liu, Xiaorui Liu, Dawei Yin, Qing Li

🏒Universities: The Hong Kong Polytechnic University,Michigan State University, North Carolina State University

πŸ“Ž Study the paper

πŸ“²Channel: @ComplexNetworkAnalysis
#paper #Graph_Machine_Learning #LLMs
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πŸ“„ Interpretable graph-based models on multimodal biomedical data integration: A technical review and benchmarking

πŸ—“ Publish year: 2025

πŸ§‘β€πŸ’»Authors: Alireza Sadeghi, Farshid Hajati, Ahmadreza Argha, ...
🏒
Universities: Clemson University, USA - University of New England & UNSW Sydney, Australia - Chinese Academy of Sciences, China.

πŸ“Ž Study paper

⚑️Channel: @ComplexNetworkAnalysis
#review #multimodal #biomedical #interpretable #graph_machine_learning #explainability
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