ArticleData in brief2024
Comprehensive dataset of heterogeneous network structures in traditional chinese medicine research.
Article in Data in brief, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
0 citing papers in PubMed.
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Corrections and comments
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Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
This dataset presents a comprehensive collection of heterogeneous network structures commonly encountered in biomedical research. The dataset encompasses diverse types of networks, including protein-protein interaction networks, herb-target interaction networks, herb-symptom interaction networks, herb-herb association networks, and symptom-symptom association networks. Each network is represented as a graph, with nodes representing entities such as proteins, herb, symptoms, and efficacy, and edges representing the relationships between these entities. The dataset provides detailed descriptions of the nodes and edges in each network, along with metadata such as node attributes where applicable. This dataset serves as a valuable resource for researchers studying complex biological systems and exploring relationships between various biomedical entities.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.