Graph representations for biology and medicine
EE-626
Media
Media
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Summary: Systems of interacting entities, modeled as graphs, are pervasive in biology and medicine. The class will cover advanced topics in signal processing and machine learning on graphs and networks, and will showcase applications of the tools in biomedicine. It will be held as an advanced seminar, which will familiarize students with recent developments in the topic, through a combination of lectures on some fundamentals on processing and analyzing data on graphs, and the presentation of original research articles that make use of these tools for scientific advances in biology and medicine.
When: Every Wednesday 10:15-12:00
Where: INF 019
Week 1: Graph representations for biology and medicine - Introduction
Background material:
- Li et al., Graph representation learning in biomedicine and healthcare, Nature Biomed. Engineering, 2022
- Zitnik et al., Current and future directions in network biology, arXiv, 2023
- Bassett et al., Network neuroscience, Nature neuroscience, 2017
- A. Avena-Koenigsberger et al., Communication dynamics in complex brain networks, Nature Rev Neurosci, 2018
- Barabasi et al., Network medicine: a network-based approach to human disease, Nature Rev Genetics, 2010
- Johnson et al., Graph Artificial Intelligence in Medicine, Annual review of biomedical data science, 2024
Week 2: Quick introduction into graph machine learning
- Lecture slides from EE-452 Network machine learning (Folder)
- Slides - Graph Machine Learning: Quick introduction (File)
Week 3: Graph theory and signal processing driven features
The following papers will be discussed:
- (Main methodological paper) Shuman et al., The emerging field of signal processing on graphs, IEEE Signal Processing Magazine, 2013
- Griffa et al., Brain structure-function coupling provides signatures for task decoding and individual fingerprinting, NeuroImage, 2022
- van Dijk et al., Recovering Gene Interactions from Single-Cell Data Using Data Diffusion, Cell, 2018
Additional background material:
- Ortega et al., Graph Signal Processing: Overview, Challenges and Applications, Proceedings of the IEEE, 2018
- Ortega, Introduction to Graph Signal Processing, Cambridge University Press, 2022
Week 4: Graph neural networks
The following papers will be discussed:
- (Main methodological paper) Wu et al., A Comprehensive Survey on Graph Neural Networks, IEEE Transactions on Neural Networks and Learning Systems, 2021
- Lee et al., Derivation of prognostic contextual histopathological features from whole-slide images of tumours via graph deep learning, Nature Biomedical Engineering, 2022
- Pegolotti et al., Learning reduced-order models for cardiovascular simulations with graph neural networks, Computers in Biology and Medicine, 2024
Additional background material:
- Bronstein et al., Geometric Deep Learning: Grids, Groups, Graphs, Geodesics, and Gauges, arXiv, 2021
Week 5: Graph Transformers
The following papers will be discussed:
- (Main methodological paper) Muller et al., Attending to Graph Transformers, Transactions on Machine Learning Research, 2024
- Zheng et al., A graph-transformer for whole slide image classification, IEEE Transactions on Medical Imaging, 2022
- Kan et al., Brain Network Transformer, NeurIPS, 2022
Week 6: Higher order representations
The following papers will be discussed:
- Kim et al., A survey on Hypergraph neural networks, KDD, 2024
- Vinas et al., Hypergraph factorization for multi-tissue gene expression imputation, Nature Machine Intelligence, 2023
- Xu et al., Hypergraph transformers for EHR-based clinical predictions, AMIA Jt Summits Transl Sci Pro, 2023
Week 7: Subgraph neural networks
The following papers will be discussed:
- Wang et al., GLASS: GNN with labeling tricks for subgraph representation learning, ICLR, 2022
- Li et al., Subgraph-aware graph kernel neural network for link prediction in biological networks, IEEE Journal of Biomedical and Health Informatics, 2024
- Luo et al., Knowledge Distillation Guided Interpretable Brain Subgraph Neural Networks for Brain Disorder Exploration, IEEE Transactions on Neural Networks and Learning Systems, 2023
Week 8: Learning from time-varying data
The following papers will be discussed:
- (Main methodological paper) Longa et al., Graph Neural Networks for Temporal Graphs: State of the Art, Open Challenges and Opportunities, Transactions on machine learning research, 2023
- Li et al., Graph-generative neural network for EEG-based epileptic seizure detection via discovery of dynamic brain functional connectivity, Nature Sci Reports, 2022
- Kim et al., Learning dynamic graph representation of brain connector with patio-temporal attention, NeurIPS, 2021
Week 9: Multimodal learning on graphs
The following papers will be discussed:
- (main methodological paper) Ektefaie et al., Multimodal learning with graphs, Nature Machine Intelligence, 2023
- Hu et al., SpaGCN: Integrating gene expression, spatial location and histology to identify spatial domains and spatially variable genes by graph convolutional network, Nature Methods, 2021
- Wang et al., MOGONET integrates multi-comics data using graph convolutional networks allowing patient classification and biomarker identification, Nature Communications, 2021
Week 10: Self-supervised learning on graphs
The following papers will be discussed:
- Liu et al., Graph Self-Supervised Learning: A Survey, IEEE Trans. on Knowledge And Data Engineering, 2023
- Huang et al., A graph self-supervised residual learning framework for domain identification and data integration of spatial transcriptomics, Nature Communications Biology, 2024
- Li et al., GMSS: Graph-Based Multi-Task Self-Supervised Learning for EEG Emotion Recognition, IEEE Transactions of Affective Computing, 2023
Week 11: Graph generative models
The following papers will be discussed:
- Zhu et al., A survey on deep graph generation: methods and applications, LoG, PMLR, 2022
- Ingraham et al., Illuminating protein space with a programmable generative model, Nature 2023
Additional reading (Optional):
- Guo et al., A Systematic Survey on Deep Generative Models for Graph Generation, 2022
- Liu et al., Graph Diffusion Transformers for Multi-conditional Molecular Generation, NeurIPS 2024
- Cretu et al., SynFlowNET: Design of diverse and novel molecules with synthesis constraints, 2024
Week 12: [Invited talk by Prof. Marianna Rapsomaniki] Modeling the tumor microenvironment with graph machine learning
Additional reading:
Brbic et al, Annotation of spatially resolved single-cell data with STELLAR, Nature Methods, 2022
Week 13: Heterogenous graphs and knowledge graphs
The following papers will be discussed:
- [Heterogenous graphs] Ma et al., Single-cell biological network inference using a heterogeneous graph transformer, Nature Communications, 2023
- [Knowledge graphs] Alsentzer et al., Few shot learning for phenotype-driven diagnosis of patients with rate genetic diseases, medRxiv, 2024
Optional readings:
- [Heterogenous graphs] Wang et al., A Survey on Heterogeneous Graph Embedding: Methods, Techniques, Applications and Sources, 2020
- [Knowledge graphs] Yang et al., A Review on Knowledge Graphs for Healthcare: Resources, Applications, and Promises, 2024
Week 14: Foundation models on graphs
The following papers will be discussed:
- Liu et al., Towards graph foundation models: A survey and beyond, arXiv, 2023
- Zhao et al., GIMLET: A unified graph-text model for instruction-based molecule zero-shot learning, NeurIPS, 2023
- Huang et al., A foundation model for clinician-centered drug repurposing, Nature Medicine, 2024