📄A quick overview of the application of machine learning techniques on biomedical graphs
💥 Technical Paper
🗓Publish year: May 10, 2022
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📲Channel: @Bioinformatics
#tachnical #graph #biomedical
💥 Technical Paper
🗓Publish year: May 10, 2022
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📲Channel: @Bioinformatics
#tachnical #graph #biomedical
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📄Graph Representation Learning in Biomedicine
🗓Publish year: 2022
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📲Channel: @Bioinformatics
#graph #biomedicine
🗓Publish year: 2022
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📲Channel: @Bioinformatics
#graph #biomedicine
👍5
Forwarded from Network Analysis Resources & Updates
📄Network-based machine learning and graph theory algorithms for precision oncology
📘Journal: npj Precision Oncology(I.F=10.092)
🗓Publish year: 2017
📎Study paper
📲Channel: @ComplexNetworkAnalysis
#paper #machine_Learning #graph
📘Journal: npj Precision Oncology(I.F=10.092)
🗓Publish year: 2017
📎Study paper
📲Channel: @ComplexNetworkAnalysis
#paper #machine_Learning #graph
👍4👏1
📃Graph representation learning in bioinformatics: trends, methods and applications
📘Journal: Briefings in Bioinformatics (I.F.=11.622)
🗓Publish year: 2022
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📲Channel: @Bioinformatics
#review #graph_representation_learning
📘Journal: Briefings in Bioinformatics (I.F.=11.622)
🗓Publish year: 2022
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📲Channel: @Bioinformatics
#review #graph_representation_learning
👍11❤2
Forwarded from Network Analysis Resources & Updates
📄Disease Prediction Using Graph Machine Learning Based on Electronic Health Data: A Review of Approaches and Trends
📘journal: HEALTHCARE-BASEL (I.F=2.8)
🗓Publish year: 2023
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📱Channel: @ComplexNetworkAnalysis
#paper #Disease #Prediction #Graph_Machine_Learning #Electronic #Health #Trends #Review
📘journal: HEALTHCARE-BASEL (I.F=2.8)
🗓Publish year: 2023
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📱Channel: @ComplexNetworkAnalysis
#paper #Disease #Prediction #Graph_Machine_Learning #Electronic #Health #Trends #Review
👍5❤2
Forwarded from Network Analysis Resources & Updates
📄Visibility graph analysis for brain: scoping review
📘 journal: Frontiers in Neuroscience (I.F=5.152)
🗓Publish year: 2023
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📲Channel: @ComplexNetworkAnalysis
#paper #graph #brain #review
📘 journal: Frontiers in Neuroscience (I.F=5.152)
🗓Publish year: 2023
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📲Channel: @ComplexNetworkAnalysis
#paper #graph #brain #review
👍11
📃Graph Neural Network approaches for single-cell data: A recent overview
🗓Publish year: 2023
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📲Channel: @Bioinformatics
#review #graph_neural_network #single_cell
🗓Publish year: 2023
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📲Channel: @Bioinformatics
#review #graph_neural_network #single_cell
👍6❤1
Forwarded from Network Analysis Resources & Updates
📄Current and future directions in network biology
🗓Publish year: 2023
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📲Channel: @ComplexNetworkAnalysis
#paper #graph #biology
🗓Publish year: 2023
📎Study paper
📲Channel: @ComplexNetworkAnalysis
#paper #graph #biology
👍5❤3👏1
Forwarded from Network Analysis Resources & Updates
📃 Graph-Theoretical Analysis of Biological Networks: A Survey
📘 Journal: Computation (I.F=2.2)
🗓 Publish year: 2023
🧑💻Author: Kayhan Erciyes
🏢University: Marmara University
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📱Channel: @ComplexNetworkAnalysis
#paper #Graph #Biological #Survey
📘 Journal: Computation (I.F=2.2)
🗓 Publish year: 2023
🧑💻Author: Kayhan Erciyes
🏢University: Marmara University
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📱Channel: @ComplexNetworkAnalysis
#paper #Graph #Biological #Survey
👍6❤2
Forwarded from Network Analysis Resources & Updates
🎞 Machine Learning with Graphs: Graph Neural Networks in Computational Biology
💥Free recorded course by Prof. Marinka Zitnik
💥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.
📽 Watch
📲Channel: @ComplexNetworkAnalysis
#video #course #Graph #GNN #Machine_Learning #computational_biology
💥Free recorded course by Prof. Marinka Zitnik
💥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.
📽 Watch
📲Channel: @ComplexNetworkAnalysis
#video #course #Graph #GNN #Machine_Learning #computational_biology
YouTube
Stanford CS224W: Machine Learning with Graphs | 2021 | Lecture 18 - GNNs in Computational Biology
For more information about Stanford’s Artificial Intelligence professional and graduate programs, visit: https://stanford.io/2XVImFC
Lecture 18 - Graph Neural Networks in Computational Biology
Jure Leskovec
Computer Science, PhD
We are glad to invite Prof.…
Lecture 18 - Graph Neural Networks in Computational Biology
Jure Leskovec
Computer Science, PhD
We are glad to invite Prof.…
👍5❤1👏1
🎓New graph learning approaches for exploring gene and protein function
📗Doctoral Thesis from ETH Zurich
🗓Publish year: 2024
📎 Study thesis
📲Channel: @Bioinformatics
#thesis #network #gene #protein #graph #deep_learning #gnn
📗Doctoral Thesis from ETH Zurich
🗓Publish year: 2024
📎 Study thesis
📲Channel: @Bioinformatics
#thesis #network #gene #protein #graph #deep_learning #gnn
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📚 Advancing Biomedicine with Graph Representation Learning: Recent Progress, Challenges, and Future Directions
💥 Book Chapter from IMIA Yearbook of Medical Informatics
🗓Publish year: 2023
🧑💻Authors: Fang Li , Yi Nian , Zenan Sun , Cui Tao
🏢University: University of Texas Health Science Center at Houston, USA
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📲Channel: @Bioinformatics
#book #chapter #Graph_representation_learning #biomedicine
💥 Book Chapter from IMIA Yearbook of Medical Informatics
🗓Publish year: 2023
🧑💻Authors: Fang Li , Yi Nian , Zenan Sun , Cui Tao
🏢University: University of Texas Health Science Center at Houston, USA
📎 Study the Chapter
📲Channel: @Bioinformatics
#book #chapter #Graph_representation_learning #biomedicine
👍4❤2👏1