Discovering regularities from traditional chinese medicine prescriptions via bipartite embedding model
- Authors: Ruan, Chunyang , Ma, Jiangang , Wang, Ye , Zhang, Yanchun , Yang, Yun
- Date: 2019
- Type: Text , Conference proceedings
- Relation: International Joint Conferences on Artificial Intelligence (IJCAI-49); Macao, China; 10th-16th August 2019; published in Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence (IJCAI-19) p. 3346-3352
- Full Text: false
- Reviewed:
- Description: Regularities analysis for prescriptions is a significant task for traditional Chinese medicine (TCM), both in inheritance of clinical experience and in improvement of clinical quality. Recently, many methods have been proposed for regularities discovery, but this task is challenging due to the quantity, sparsity and free-style of prescriptions. In this paper, we address the specific problem of regularities discovery and propose a graph embedding based framework for regularities discovery for massive prescriptions. We model this task as a relation prediction in which the correlation of two herbs or of herb and symptom are incorporated to characterize the different relationships. Specifically, we first establish a heterogeneous network with herbs and symptoms as its nodes. We develop a bipartite embedding model termed HS2Vec to detect regularities, which explores multiple relations of herbherb, and herb-symptom based on the heterogeneous network. Experiments on four real-world datasets demonstrate that the proposed framework is very effective for regularities discovery.
Adversarial heterogeneous network embedding with metapath attention mechanism
- Authors: Ruan, Chunyang , Wang, Ye , Ma, Jiangang , Zhang, Yanchun , Chen, Xintian
- Date: 2019
- Type: Text , Journal article
- Relation: Journal of Computer Science and Technology Vol. 34, no. 6 (2019), p. 1217-1229
- Full Text: false
- Reviewed:
- Description: Heterogeneous information network (HIN)-structured data provide an effective model for practical purposes in real world. Network embedding is fundamental for supporting the network-based analysis and prediction tasks. Methods of network embedding that are currently popular normally fail to effectively preserve the semantics of HIN. In this study, we propose AGA2Vec, a generative adversarial model for HIN embedding that uses attention mechanisms and meta-paths. To capture the semantic information from multi-typed entities and relations in HIN, we develop a weighted meta-path strategy to preserve the proximity of HIN. We then use an autoencoder and a generative adversarial model to obtain robust representations of HIN. The results of experiments on several real-world datasets show that the proposed approach outperforms state-of-the-art approaches for HIN embedding. © 2019, Springer Science+Business Media, LLC & Science Press, China.
THCluster: herb supplements categorization for precision traditional Chinese medicine
- Authors: Ruan, Chunyang , Wang, Ye , Zhang, Yanchun , Ma, Jiangang , Chen, Huijuan , Aickelin, Uwe , Zhu, Shanfeng , Zhang, Ting
- Date: 2020
- Type: Text , Conference proceedings
- Relation: 2017 IEEE International Conference on Bioinformatics and Biomedicine (BIBM);Kansas City, MO, USA; 13-16 Nov. 2017 p. 417-424
- Full Text: false
- Reviewed:
- Description: There has been a continuing demand for traditional and complementary medicine worldwide. A fundamental and important topic in Traditional Chinese Medicine (TCM) is to optimize the prescription and to detect herb regularities from TCM data. In this paper, we propose a novel clustering model to solve this general problem of herb categorization, a pivotal task of prescription optimization and herb regularities. The model utilizes Random Walks method, Bayesian rules and Expectation Maximization(EM) models to complete a clustering analysis effectively on a heterogeneous information network. We performed extensive experiments on the real-world datasets and compared our method with other algorithms and experts. Experimental results have demonstrated the effectiveness of the proposed model for discovering useful categorization of herbs and its potential clinical manifestations.