Dataset Information

Effective chemical constituent's identification of extracting solution from herbs - a review.

ABSTRACT:

Background and objective

The aim of this article, was the definition, and targets of possible mechanisms of Yin lai Decoction by retrieving the herbals in the decoction and integrating information from multi-databases, which can be guidance for the followed experimental study.

Matherials and methods

The information from multi-databases was integrated.

Results

In the study, we discovered 92 possible gene targets of Yin lai Decoction, 87% of which appeared in the targets of Flos Lonicerae, Fructus Forsythiae and Scutellaria baicalensis Georgi. The function of these targets focuses on the anti-infection and regulating immune system, and metabolism of the body.

Conclusion

Yin lai Decoction may affect the process of metabolism, immune response and infection by impact on the 92 possible gene targets, then it can mitigate the damage caused by infection and promote the body to health. But the definite mechanisms must be explored by further experimental study.

SUBMITTER: Liu T

PROVIDER: S-EPMC4202420 | biostudies-literature | 2014

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Effective chemical constituent's identification of extracting solution from herbs - a review.

Liu Tiegang T Zhen Jianhua J Yu He H Zhang Wang W Li Xiaofei X Lv Guokai G Murtaza Ghulam G Gu Xiaohong X

African journal of traditional, complementary, and alternative medicines : AJTCAM 20140403 3

<h4>Background and objective</h4>The aim of this article, was the definition, and targets of possible mechanisms of Yin lai Decoction by retrieving the herbals in the decoction and integrating information from multi-databases, which can be guidance for the followed experimental study.<h4>Matherials and methods</h4>The information from multi-databases was integrated.<h4>Results</h4>In the study, we discovered 92 possible gene targets of Yin lai Decoction, 87% of which appeared in the targets of F ...[more]

PMID: 25371564

Similar Datasets

Project description:BackgroundEnzymatic and chemical reactions are key for understanding biological processes in cells. Curated databases of chemical reactions exist but these databases struggle to keep up with the exponential growth of the biomedical literature. Conventional text mining pipelines provide tools to automatically extract entities and relationships from the scientific literature, and partially replace expert curation, but such machine learning frameworks often require a large amount of labeled training data and thus lack scalability for both larger document corpora and new relationship types.ResultsWe developed an application of Snorkel, a weakly supervised learning framework, for extracting chemical reaction relationships from biomedical literature abstracts. For this work, we defined a chemical reaction relationship as the transformation of chemical A to chemical B. We built and evaluated our system on small annotated sets of chemical reaction relationships from two corpora: curated bacteria-related abstracts from the MetaCyc database (MetaCyc_Corpus) and a more general set of abstracts annotated with MeSH (Medical Subject Headings) term Bacteria (Bacteria_Corpus; a superset of MetaCyc_Corpus). For the MetaCyc_Corpus, we obtained 84% precision and 41% recall (55% F1 score). Extending to the more general Bacteria_Corpus decreased precision to 62% with only a four-point drop in recall to 37% (46% F1 score). Overall, the Bacteria_Corpus contained two orders of magnitude more candidate chemical reaction relationships (nine million candidates vs 68,0000 candidates) and had a larger class imbalance (2.5% positives vs 5% positives) as compared to the MetaCyc_Corpus. In total, we extracted 6871 chemical reaction relationships from nine million candidates in the Bacteria_Corpus.ConclusionsWith this work, we built a database of chemical reaction relationships from almost 900,000 scientific abstracts without a large training set of labeled annotations. Further, we showed the generalizability of our initial application built on MetaCyc documents enriched with chemical reactions to a general set of articles related to bacteria.

Dataset Information

Effective chemical constituent's identification of extracting solution from herbs - a review.

Background and objective

Matherials and methods

Results

Conclusion

Publications

Effective chemical constituent's identification of extracting solution from herbs - a review.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets