Discovering Regularities from Traditional Chinese Medicine Prescriptions via Bipartite Embedding Model
Authors: Chunyang Ruan, Jiangang Ma, Ye Wang, Yanchun Zhang, Yun Yang
IJCAI 2019 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experiments on four real-world datasets demonstrate that the proposed framework is very effective for regularities discovery. The proposed method HS2Vec is tested on four real-world TCM datasets and compared to state-of-the-art methods and to TCM doctors. |
| Researcher Affiliation | Academia | 1Fudan University, Shanghai,China 2Zhejiang Lab, Hanghzou, China 3Federation University Australia, Melbourne, Australia 4Victoria University, Melbourne, Australia 5Longhua Hospital Shanghai University of Traditional Chinese Medicine, Shanghai, China |
| Pseudocode | No | The paper does not contain any structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide any concrete access information (e.g., specific links, explicit statements of code release) for the source code of the methodology described. |
| Open Datasets | Yes | TRE [Wan et al., 2015]: The dataset integrates herbs, symptoms, diseases and their correlations from the Chinese TCM texts. TCMSP [Ru et al., 2014]: The dataset describes a pharmacology information TCM, which includes herbs, diseases, chemicals, targets and their correlations. TCMGe DIT [Fang et al., 2008]: The dataset provides association information about genes, diseases, TCM effects and TCM ingredients automatically mined from vast amount of biomedical literature. |
| Dataset Splits | No | The paper mentions using four datasets for experiments but does not provide specific details on how these datasets were split into training, validation, or test sets (e.g., percentages, sample counts, or explicit cross-validation schemes). |
| Hardware Specification | No | The paper does not explicitly describe the specific hardware (e.g., GPU/CPU models, memory) used to run its experiments. |
| Software Dependencies | No | The paper does not provide specific version numbers for any software dependencies or libraries used in the implementation or experimentation. |
| Experiment Setup | Yes | In Section 5.5 "Parameter Sensitivity", the paper discusses the influence of parameters like "dimension d of the latent space" and "penalty coefficient α in the Eq.(7)", providing analysis on how varying these parameters affects performance. |