Summary of Boosting Drug-disease Association Prediction For Drug Repositioning Via Dual-feature Extraction and Cross-dual-domain Decoding, by Enqiang Zhu et al.
Boosting drug-disease association prediction for drug repositioning via dual-feature extraction and cross-dual-domain decoding
by Enqiang Zhu, Xiang Li, Chanjuan Liu, Nikhil R. Pal
First submitted to arxiv on: 16 Jul 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Quantitative Methods (q-bio.QM)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The proposed Dual-Feature Drug Repurposing Neural Network (DFDRNN) model addresses limitations in existing drug repositioning methods by incorporating a self-attention mechanism to extract neighbor feature information. The DFDRNN model utilizes dual-feature extraction modules for single-domain and cross-domain feature extraction, enabling more accurate encoding of drugs and diseases. A cross-dual-domain decoder predicts drug-disease associations in both domains, outperforming six state-of-the-art methods on four benchmark datasets with an average AUROC of 0.946 and an average AUPR of 0.597. Case studies demonstrate the model’s potential in real-world scenarios. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The researchers developed a new way to find new uses for approved drugs. They wanted to make sure their approach was accurate, so they created a special kind of computer program called the Dual-Feature Drug Repurposing Neural Network (DFDRNN). This program looks at two types of information: how similar two things are and how closely related they are. The program is good at finding connections between drugs and diseases that other approaches might miss. It even works better than some other methods that were developed earlier. The researchers tested their program on several different sets of data and found that it was very effective. They also showed that this program could be used to help find new treatments for real-world diseases. |
Keywords
» Artificial intelligence » Decoder » Feature extraction » Neural network » Self attention
