دورية أكاديمية
Integration of Neighbor Topologies Based on Meta-Paths and Node Attributes for Predicting Drug-Related Diseases
| العنوان: | Integration of Neighbor Topologies Based on Meta-Paths and Node Attributes for Predicting Drug-Related Diseases |
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| المؤلفون: | Ping Xuan, Zixuan Lu, Tiangang Zhang, Yong Liu, Toshiya Nakaguchi |
| المصدر: | International Journal of Molecular Sciences, Vol 23, Iss 7, p 3870 (2022) |
| بيانات النشر: | MDPI AG, 2022. |
| سنة النشر: | 2022 |
| المجموعة: | LCC:Biology (General) LCC:Chemistry |
| مصطلحات موضوعية: | drug–disease association prediction, neighbor topology learning based on meta-paths, pairwise node attribute encoding, multiple drug–disease heterogeneous networks, fully connected neural networks and autoencoder based on CNN, Biology (General), QH301-705.5, Chemistry, QD1-999 |
| الوصف: | Identifying new disease indications for existing drugs can help facilitate drug development and reduce development cost. The previous drug–disease association prediction methods focused on data about drugs and diseases from multiple sources. However, they did not deeply integrate the neighbor topological information of drug and disease nodes from various meta-path perspectives. We propose a prediction method called NAPred to encode and integrate meta-path-level neighbor topologies, multiple kinds of drug attributes, and drug-related and disease-related similarities and associations. The multiple kinds of similarities between drugs reflect the degrees of similarity between two drugs from different perspectives. Therefore, we constructed three drug–disease heterogeneous networks according to these drug similarities, respectively. A learning framework based on fully connected neural networks and a convolutional neural network with an attention mechanism is proposed to learn information of the neighbor nodes of a pair of drug and disease nodes. The multiple neighbor sets composed of different kinds of nodes were formed respectively based on meta-paths with different semantics and different scales. We established the attention mechanisms at the neighbor-scale level and at the neighbor topology level to learn enhanced neighbor feature representations and enhanced neighbor topological representations. A convolutional-autoencoder-based module is proposed to encode the attributes of the drug–disease pair in three heterogeneous networks. Extensive experimental results indicated that NAPred outperformed several state-of-the-art methods for drug–disease association prediction, and the improved recall rates demonstrated that NAPred was able to retrieve more actual drug–disease associations from the top-ranked candidates. Case studies on five drugs further demonstrated the ability of NAPred to identify potential drug-related disease candidates. |
| نوع الوثيقة: | article |
| وصف الملف: | electronic resource |
| اللغة: | English |
| تدمد: | 1422-0067 1661-6596 26247429 |
| Relation: | https://www.mdpi.com/1422-0067/23/7/3870; https://doaj.org/toc/1661-6596; https://doaj.org/toc/1422-0067 |
| DOI: | 10.3390/ijms23073870 |
| URL الوصول: | https://doaj.org/article/cb4d0bf2624742939f94788178309b24 |
| رقم الأكسشن: | edsdoj.b4d0bf2624742939f94788178309b24 |
| قاعدة البيانات: | Directory of Open Access Journals |
Full Text Finder | تدمد: | 14220067 16616596 26247429 |
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| DOI: | 10.3390/ijms23073870 |