Evidence map›Paper›PMID 38886444›Full record

ArticleScientific reports2024

Knowledge graph-derived feed efficiency analysis via pig gut microbiota.

Junmei Zhang, Qin Jiang, Zhihong Du, Yilin Geng, Yuren Hu, Qichang Tong, Yunfeng Song, Hong-Yu Zhang, Xianghua Yan, Zaiwen Feng

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Scientific reports, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

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.

2 · The registry

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.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

10 authors.

Junmei Zhang *National Key Laboratory of Agricultural Microbiology, College of Informatics, College of Animal Sciences and Technology, College of Veterinary Medicine, Huazhong Agricultural University, Wuhan, 430070, China.
Qin Jiang *National Key Laboratory of Agricultural Microbiology, College of Informatics, College of Animal Sciences and Technology, College of Veterinary Medicine, Huazhong Agricultural University, Wuhan, 430070, China.
Zhihong DuNational Key Laboratory of Agricultural Microbiology, College of Informatics, College of Animal Sciences and Technology, College of Veterinary Medicine, Huazhong Agricultural University, Wuhan, 430070, China.
Yilin GengNational Key Laboratory of Agricultural Microbiology, College of Informatics, College of Animal Sciences and Technology, College of Veterinary Medicine, Huazhong Agricultural University, Wuhan, 430070, China.
Yuren HuNational Key Laboratory of Agricultural Microbiology, College of Informatics, College of Animal Sciences and Technology, College of Veterinary Medicine, Huazhong Agricultural University, Wuhan, 430070, China.
Qichang TongNational Key Laboratory of Agricultural Microbiology, College of Informatics, College of Animal Sciences and Technology, College of Veterinary Medicine, Huazhong Agricultural University, Wuhan, 430070, China.
Yunfeng SongNational Key Laboratory of Agricultural Microbiology, College of Informatics, College of Animal Sciences and Technology, College of Veterinary Medicine, Huazhong Agricultural University, Wuhan, 430070, China.
Hong-Yu ZhangNational Key Laboratory of Agricultural Microbiology, College of Informatics, College of Animal Sciences and Technology, College of Veterinary Medicine, Huazhong Agricultural University, Wuhan, 430070, China.
Xianghua YanNational Key Laboratory of Agricultural Microbiology, College of Informatics, College of Animal Sciences and Technology, College of Veterinary Medicine, Huazhong Agricultural University, Wuhan, 430070, China.
Zaiwen FengNational Key Laboratory of Agricultural Microbiology, College of Informatics, College of Animal Sciences and Technology, College of Veterinary Medicine, Huazhong Agricultural University, Wuhan, 430070, China. Zaiwen.Feng@mail.hzau.edu.cn.

Funding

Fundamental Research Funds for the Chinese Central Universities 2662023XXPY004National Digital Animal Husbandry (Pig) Innovation Center Project 203-61022300002National Natural Science Foundation of China 320021190
6 · The paper itself

Abstract

Feed efficiency (FE) is essential for pig production, has been reported to be partially explained by gut microbiota. Despite an extensive body of research literature to this topic, studies regarding the regulation of feed efficiency by gut microbiota remain fragmented and mostly confined to disorganized or semi-structured unrestricted texts. Meanwhile, structured databases for microbiota analysis are available, yet they often lack a comprehensive understanding of the associated biological processes. Therefore, we have devised an approach to construct a comprehensive knowledge graph by combining unstructured textual intelligence with structured database information and applied it to investigate the relationship between pig gut microbes and FE. Firstly, we created the pgmReading knowledge base and the domain ontology of pig gut microbiota by annotating, extracting, and integrating semantic information from 157 scientific publications. Secondly, we created the pgmPubtator by utilizing PubTator to expand the semantic information related to microbiota. Thirdly, we created the pgmDatabase by mapping and combining the ADDAGMA, gutMGene, and KEGG databases based on the ontology. These three knowledge bases were integrated to form the Pig Gut Microbial Knowledge Graph (PGMKG). Additionally, we created five biological query cases to validate the performance of PGMKG. These cases not only allow us to identify microbes with the most significant impact on FE but also provide insights into the metabolites produced by these microbes and the associated metabolic pathways. This study introduces PGMKG, mapping key microbes in pig feed efficiency and guiding microbiota-targeted optimization.

Indexed as

Animal FeedGastrointestinal MicrobiomeAnimalsDatabases, FactualKnowledge BasesSwineFeed efficiencyGut microbiotaKnowledge graphPig

Identifiers

PMID38886444
PMCPMC11182767

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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.