https://github.com/multiomics-analytics-group/course_graph_machine_learning

This is part of an AI course
https://github.com/multiomics-analytics-group/course_graph_machine_learning

Last synced: about 1 year ago
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This is part of an AI course

• Host: GitHub
• URL: https://github.com/multiomics-analytics-group/course_graph_machine_learning
• Owner: Multiomics-Analytics-Group
• License: gpl-3.0
• Created: 2025-01-10T02:57:05.000Z (over 1 year ago)
• Default Branch: main
• Last Pushed: 2025-01-13T15:12:35.000Z (over 1 year ago)
• Last Synced: 2025-03-26T02:18:55.561Z (about 1 year ago)
• Language: Jupyter Notebook
• Size: 13.8 MB
• Stars: 0
• Watchers: 3
• Forks: 1
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

Awesome Lists containing this project

README

          
# Course 27666 AI-guided Protein Science

# Graph Machine Learning

## Course Structure

| **Topics**   |                  Description       |         Hands-on            |Models           |

|--------------|-------------------------|------------------------------|-----------------|

|[Graphs](slides/Graphs.pdf)| Introduction to Graph Biology |  [**Data to Graphs**](notebooks/data2graphs.ipynb) [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/Multiomics-Analytics-Group/course_graph_machine_learning/blob/main/notebooks/data2graphs.ipynb)     |                 |

| [Graph Machine Learning](slides/GraphMachineLearning.pdf) |  An overview of the Graph Machine Learnign tasks and methods | 1. [**Intro to PyG**](notebooks/PyG_Introduction.ipynb) [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/Multiomics-Analytics-Group/course_graph_machine_learning/blob/main/notebooks/PyG_Introduction.ipynb)  / 2. [**Shallow Embeddings**](notebooks/shallow_embeddings.ipynb) [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/Multiomics-Analytics-Group/course_graph_machine_learning/blob/main/notebooks/shallow_embeddings.ipynb) | [Node2Vec](https://arxiv.org/abs/1607.00653) / [DeepWalk](https://arxiv.org/abs/1403.6652)  |

| [Graph Neural Networks](slides/GraphNeuralNetworks.pdf) | Key concepts in GNNs |1. [**GNN Prediction**](notebooks/GNN_prediction.ipynb) [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/Multiomics-Analytics-Group/course_graph_machine_learning/blob/main/notebooks/GNN_prediction.ipynb) / 2. [**Graph-level Prediction**](notebooks/graph_level_prediction.ipynb) [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/Multiomics-Analytics-Group/course_graph_machine_learning/blob/main/notebooks/graph_level_prediction.ipynb) | [GNN](https://pytorch-geometric.readthedocs.io/en/2.6.1/cheatsheet/gnn_cheatsheet.html) |

## Relevant links

[IvLabs](https://ivlabs.github.io/resources/graph-representation-learning/)

[Geometric Deep Learning](https://geometricdeeplearning.com/lectures/)

[GNNs](https://github.com/SeongokRyu/Graph-neural-networks)

[Stanford cs 224w](https://medium.com/stanford-cs224w)

[Link prediction on Heterogeneous graphs](https://medium.com/@pytorch_geometric/link-prediction-on-heterogeneous-graphs-with-pyg-6d5c29677c70)

[MLNTeam-Unical](https://mlnteam-unical.github.io/resources/)

[Computational Network Biology](https://compnetbiocourse.discovery.wisc.edu/)

[Combining Embeddings](https://medium.com/mantisnlp/how-to-combine-several-embeddings-models-8e7bc9a00330)