Evidence map›Paper›PMID 35654892›Full record

ArticleNature communications2022

Machine learning aided construction of the quorum sensing communication network for human gut microbiota.

Shengbo Wu, Jie Feng, Chunjiang Liu, Hao Wu, Zekai Qiu, Jianjun Ge, Shuyang Sun, Xia Hong, Yukun Li, Xiaona Wang and 3 more

Abstract read
In one paragraph

Article in Nature communications, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 41 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
41citing papers in PubMed, 1 pooled it
–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

41 citing papers in PubMed, 1 synthesis or guideline pooled it.

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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

13 authors.

Shengbo WuSchool of Chemical Engineering and Technology, Tianjin University, Tianjin, 300072, China.ORCID 0000-0001-7497-2935
Jie FengSchool of Computer Science and Technology, College of Intelligence and Computing, Tianjin University, Tianjin, 300350, China.ORCID 0000-0001-7892-3401
Chunjiang LiuSchool of Chemical Engineering and Technology, Tianjin University, Tianjin, 300072, China.ORCID 0000-0003-1659-3579
Hao WuZhejiang Shaoxing Research Institute of Tianjin University, Shaoxing, 312300, China.ORCID 0000-0002-1502-1758
Zekai QiuSchool of Chemical Engineering and Technology, Tianjin University, Tianjin, 300072, China.ORCID 0000-0003-1438-2014
Jianjun GeSchool of Chemical Engineering and Technology, Tianjin University, Tianjin, 300072, China.ORCID 0000-0001-5658-3653
Shuyang SunSchool of Chemical Engineering and Technology, Tianjin University, Tianjin, 300072, China.ORCID 0000-0002-5747-5205
Xia HongSchool of Chemical Engineering and Technology, Tianjin University, Tianjin, 300072, China.ORCID 0000-0002-2369-2877
Yukun LiSchool of Chemical Engineering and Technology, Tianjin University, Tianjin, 300072, China.ORCID 0000-0003-3530-4613
Xiaona WangSchool of Chemical Engineering and Technology, Tianjin University, Tianjin, 300072, China.ORCID 0000-0001-9064-3349
Aidong YangDepartment of Engineering Science, University of Oxford, Oxford, OX1 3PJ, UK. aidong.yang@eng.ox.ac.uk.ORCID 0000-0001-5974-247X
Fei GuoSchool of Computer Science and Engineering, Central South University, Changsha, 410083, China. guofei@csu.edu.cn.ORCID 0000-0001-8346-0798
Jianjun QiaoSchool of Chemical Engineering and Technology, Tianjin University, Tianjin, 300072, China. jianjunq@tju.edu.cn.ORCID 0000-0002-8126-2074

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Quorum sensing (QS) is a cell-cell communication mechanism that connects members in various microbial systems. Conventionally, a small number of QS entries are collected for specific microbes, which is far from being able to fully depict communication-based complex microbial interactions in human gut microbiota. In this study, we propose a systematic workflow including three modules and the use of machine learning-based classifiers to collect, expand, and mine the QS-related entries. Furthermore, we develop the Quorum Sensing of Human Gut Microbes (QSHGM) database ( http://www.qshgm.lbci.net/ ) including 28,567 redundancy removal entries, to bridge the gap between QS repositories and human gut microbiota. With the help of QSHGM, various communication-based microbial interactions can be searched and a QS communication network (QSCN) is further constructed and analysed for 818 human gut microbes. This work contributes to the establishment of the QSCN which may form one of the key knowledge maps of the human gut microbiota, supporting future applications such as new manipulations to synthetic microbiota and potential therapies to gut diseases.

Indexed as

Gastrointestinal MicrobiomeMicrobiotaHumansMachine LearningMicrobial InteractionsQuorum Sensing

Identifiers

PMID35654892
PMCPMC9163137

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Registered trials

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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.