DeepResponse: Large Scale Prediction of Cancer Cell Line Drug Response with Deep Learning Based Pharmacogenomic Modelling
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Updated
Jun 9, 2024 - Python
DeepResponse: Large Scale Prediction of Cancer Cell Line Drug Response with Deep Learning Based Pharmacogenomic Modelling
Assignments and presentation developed in the scope of the Deep Learning discipline, lectured by Professor Dário Oliveira (FGV EMAp). Co-authored with @anacarolerthal.
Graph molecular learning to predict blood-brain-barrier penetration and CNS drug delivery.
Code for VN-Solver: Vision-based Neural Solver for Combinatorial Optimization over Graphs
Pretraining Techniques for Graph Transformers
Implementation of Power Law Graph Transformer for Machine Translation and Representation Learning.
Repository for "Integrative Graph-Transformer Framework for Histopathology Whole Slide Image Representation and Classification""
Codebase of paper "Balancing structure and position information in Graph Transformer network with a learnable node embedding"
Code for our paper "Attending to Graph Transformers"
VCR-Graphormer: A Mini-batch Graph Transformer via Virtual Connections, ICLR 2024
Welcome to the Graph Neural Networks (06838-01) class repository for the Department of Artificial Intelligence at the Catholic University of Korea. This platform is dedicated to sharing and archiving lecture materials such as practices, assignments, and sample codes for the class.
Codebase for paper: "Improving GCN with Transformer layer in social-based items recommendation"
DeepResponse: Large Scale Prediction of Cancer Cell Line Drug Response with Deep Learning Based Pharmacogenomic Modelling
Test graph isomorphism with 1-WL for different graph classes and labelings
KDD-23 Automated 3D Pre-Training for Molecular Property Prediction
The Graph Representation Learning Framework developed by NS Lab @ CUK.
[AAAI'23] MulGT: Multi-task Graph-Transformer with Task-aware Knowledge Injection and Domain Knowledge-driven Pooling for Whole Slide Image Analysis
Welcome to the Graph Neural Networks (06838-01) class repository for the Department of Artificial Intelligence at the Catholic University of Korea. This platform is dedicated to sharing and archiving lecture materials such as practices, assignments, and sample codes for the class.
Community-aware Graph Transformer (CGT) is a novel Graph Transformer model that utilizes community structures to address node degree biases in message-passing mechanism and developed by NS Lab @ CUK based on pure PyTorch backend.
Official Pytorch implementation of NeuralWalker
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