Bioinformatics
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Bioinformatics, Computational Biology & Systems Biology

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πŸ“„Network-based machine learning and graph theory algorithms for precision oncology

πŸ“˜Journal: npj Precision Oncology(I.F=10.092)

πŸ—“Publish year: 2017

πŸ“ŽStudy paper

πŸ“²Channel: @ComplexNetworkAnalysis
#paper #machine_Learning #graph
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πŸ“ƒGraph representation learning in bioinformatics: trends, methods and applications

πŸ“˜Journal: Briefings in Bioinformatics (I.F.=11.622)
πŸ—“Publish year: 2022

πŸ“Ž Study the paper

πŸ“²Channel: @Bioinformatics
#review #graph_representation_learning
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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“„Visibility graph analysis for brain: scoping review

πŸ“˜ journal: Frontiers in Neuroscience (I.F=5.152)
πŸ—“
Publish year: 2023

πŸ“ŽStudy paper

πŸ“²Channel: @ComplexNetworkAnalysis
#paper #graph #brain #review
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πŸ“ƒGraph Neural Network approaches for single-cell data: A recent overview

πŸ—“Publish year: 2023

πŸ“Ž Study the paper

πŸ“²Channel: @Bioinformatics
#review #graph_neural_network #single_cell
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πŸ“„Current and future directions in network biology

πŸ—“Publish year: 2023

πŸ“ŽStudy paper

πŸ“²Channel: @ComplexNetworkAnalysis
#paper #graph #biology
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πŸ“ƒ Graph-Theoretical Analysis of Biological Networks: A Survey

πŸ“˜ Journal: Computation (I.F=2.2)
πŸ—“ Publish year: 2023

πŸ§‘β€πŸ’»Author: Kayhan Erciyes
🏒University: Marmara University

πŸ“Ž Study the paper

πŸ“±Channel: @ComplexNetworkAnalysis
#paper #Graph #Biological #Survey
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🎞 Machine Learning with Graphs: Graph Neural Networks in Computational Biology

πŸ’₯Free recorded course by Prof. Marinka Zitnik

πŸ’₯In this lecture, Prof. Marinka gives an overview of why graph learning techniques can greatly help with computational biology research. Concretely, this talk covers 3 exemplar use cases: (1) Discovering safe drug-drug combinations via multi-relational link prediction on heterogenous knowledge graphs; (2) Classify patient outcomes and diseases via learning subgraph embeddings; and (3) Learning effective disease treatments through few-shot learning for graphs.

πŸ“½ Watch

πŸ“²Channel: @ComplexNetworkAnalysis

#video #course #Graph #GNN #Machine_Learning #computational_biology
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πŸŽ“New graph learning approaches for exploring gene and protein function

πŸ“—Doctoral Thesis from ETH Zurich

πŸ—“Publish year: 2024

πŸ“Ž Study thesis

πŸ“²Channel: @Bioinformatics
#thesis #network #gene #protein #graph #deep_learning #gnn
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πŸ“š Advancing Biomedicine with Graph Representation Learning: Recent Progress, Challenges, and Future Directions
πŸ’₯ Book Chapter from IMIA Yearbook of Medical Informatics

πŸ—“Publish year: 2023

πŸ§‘β€πŸ’»
Authors: Fang Li , Yi Nian , Zenan Sun , Cui Tao
🏒University: University of Texas Health Science Center at Houston, USA

πŸ“Ž Study the Chapter

πŸ“²Channel: @Bioinformatics
#book #chapter #Graph_representation_learning #biomedicine
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πŸ“‘ Application of graph theory in liver research: A review

πŸ—“ Publish year: 2024
πŸ“•Journal: Portal Hypertension & Cirrhosis

πŸ§‘β€πŸ’»Authors: Xumei Hu, Longyu Sun, Rencheng Zheng, ...
🏒
Universities: Fudan University, China

πŸ“Ž Study paper

⚑️Channel: @ComplexNetworkAnalysis
#review #liver #graph
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πŸ“ƒMolecule generation for drug design: A graph learning perspective

πŸ“— Journal: Fundamental Research (I.F.=5.7)
πŸ—“ Publish year: 2024

πŸ§‘β€πŸ’»Authors: Nianzu Yang, Huaijin Wu, Kaipeng Zeng, ...

🏒Universities: Shanghai Jiao Tong University, China

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πŸ“²Channel: @Bioinformatics
#review #molecule #drug #graph
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πŸ“‘ Graph Neural Networks in Modern AI-aided Drug Discovery

πŸ—“Publish year: 2025

πŸ§‘β€πŸ’»Authors: Odin Zhang, Haitao Lin, Xujun Zhang, ...
🏒Universities: Zhejiang University, Hangzhou & Westlake University, China - Harvard University, USA

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πŸ“²Channel: @Bioinformatics
#review #drug #ai #gnn #graph_neural_network
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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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