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Posts
Upcoming UCL Computational Cancer Collaboratorium Hackathon!
Published:
let’s goooooooooo tackling some Computational Cancer challenges at Dec. 10!
portfolio
Portfolio item number 1
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Short description of portfolio item number 1
Portfolio item number 2
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Short description of portfolio item number 2
publications
Paper incoming
Published in This paper is coming soon, 2024
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Recommended citation: This paper is coming soon
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Predicting Drug-Drug Interactions Using Heterogeneous Graph Neural Networks: HGNN-DDI
Published in Applied and Computational Engineering, Volume 79, 2024
This research centers on predicting drug-drug interactions (DDIs) using a novel approach involving graph neural networks (GNNs) with integrated attention mechanisms. In this method, drugs and proteins are depicted as nodes within a heterogeneous graph. This graph is characterized by different types of edges symbolizing not only DDIs but also drug-protein interactions (DPIs) and protein-protein interactions (PPIs). To analyze the chemical structures of drugs, we employ a pretrained model named ChemBERTa, which utilizes simplified molecular input line entry system (SMILES) strings. The similarity between drug structures based on their SMILES strings is determined using the RDkit tool. Our model is designed to establish and link heterogeneous graph neural networks, taking into account the DPIs and PPIs as key input data. For the final prediction of interaction types between various drugs, we use the Multi-Layer Perception (MLP) technique. The objective is to enhance the accuracy of DDI predictions by factoring in additional data on both drug-protein and protein-protein interactions. The forecasted DDIs are presented with associated probabilities, offering valuable insights to healthcare professionals. These insights are crucial for assessing the potential risks and advantages of combining different drugs, particularly for patients with diseases at different stages of progression.
Recommended citation: Liu, H., Li, S., & Yu, Z. (2024). Predicting drug-drug interactions using heterogeneous graph neural networks: HGNN-DDI. Applied and Computational Engineering, 79, 77–89.
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talks
Talk 1 on Relevant Topic in Your Field
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This is a description of your talk, which is a markdown files that can be all markdown-ified like any other post. Yay markdown!
Conference Proceeding talk 3 on Relevant Topic in Your Field
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This is a description of your conference proceedings talk, note the different field in type. You can put anything in this field.
teaching
Teaching Assistance
IGCSE course, SuZhou EA, 2014
One-to-one teaching in IGCSE Mathematics and Computer Science.