Details

Autor(en) / Beteiligte

• Liu, Huiying
• Fu, Xiangzheng
• Chen, Haiting
• Shang, Jun
• Zhou, Haoyu
• Zhe, Wang
• Yao, Xiaojun

Titel

Developing explainable models for lncRNA-Targeted drug discovery using graph autoencoders

Ist Teil von

• Future generation computer systems, 2024-11, Vol.160, p.29-39

Ort / Verlag

Elsevier B.V

Erscheinungsjahr

2024

Quelle

Access via ScienceDirect (Elsevier)

Beschreibungen/Notizen

• The application of explainable artificial intelligence technology is crucial in lncRNA-targeted drug discovery, given lncRNA’s role in regulating a broad spectrum of diseases, including cancer. Current advanced deep learning models efficiently predict the association between lncRNA and disease, facilitating the discovery of potential lncRNA-targeted drug. However, these models have notable limitations, including a tendency to overfit due to their focus on integrating various similarity networks for feature extraction of lncRNA and diseases. Additionally, they employ complex classifiers to predict unknown lncRNA-disease associations (LDAs), resulting in reduced interpretability. We designed an explainable model named EM-LTDD, utilizing graph autoencoders (GAE) and self-supervised learning strategies, aimed at identifying potential drugs targeting lncRNA. The EM-LTDD model improves self-supervised training by employing a novel strategy that masks parts of the known lncRNA-disease network. This approach not only enhances the model’s interpretability but also mitigates the impact of noise. Furthermore, the EM-LTDD model reconstructs the lncRNA-disease network with a robust collaborative decoder, minimizing information loss caused by occlusion and improving node representation. Experimental results demonstrate the effectiveness of our model compared to existing leading models, promising to significantly aid in the discovery and design of lncRNA-targeted drugs. Our code and data are available at: https://github.com/huiyingliulevy/EM-LTDD. •This study proposes an explainable model to identify potential lncRNA-targeted drugs.•This study presents a random masking approach to enhance self-supervised training.•This study employs a collaborative decoder to enhance node representation quality.•Experiments were conducted on public datasets to validate the model’s performance.