Evidence map›Paper›PMID 33468221›Full record

ArticleBioData mining2021

LightCUD: a program for diagnosing IBD based on human gut microbiome data.

Congmin Xu, Man Zhou, Zhongjie Xie, Mo Li, Xi Zhu, Huaiqiu Zhu

Abstract read
In one paragraph

Article in BioData mining, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
9citing 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

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

  1. Pooled it
  2. Article
  3. Review
  4. Review
  5. Article
  6. Article
  7. Review
  8. Review
  9. 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

6 authors.

Congmin XuState Key Laboratory for Turbulence and Complex Systems, Department of Biomedical Engineering, College of Engineering, Peking University, Beijing, 100871, China.
Man ZhouState Key Laboratory for Turbulence and Complex Systems, Department of Biomedical Engineering, College of Engineering, Peking University, Beijing, 100871, China.
Zhongjie XieState Key Laboratory for Turbulence and Complex Systems, Department of Biomedical Engineering, College of Engineering, Peking University, Beijing, 100871, China.
Mo LiState Key Laboratory for Turbulence and Complex Systems, Department of Biomedical Engineering, College of Engineering, Peking University, Beijing, 100871, China.
Xi ZhuDepartment of Critical Care Medicine, Peking University Third Hospital, Beijing, 100191, China. xizhuccm@163.com.
Huaiqiu ZhuState Key Laboratory for Turbulence and Complex Systems, Department of Biomedical Engineering, College of Engineering, Peking University, Beijing, 100871, China. hqzhu@pku.edu.cn.ORCID http://orcid.org/0000-0002-6376-218X

Funding

National Natural Science Foundation of China 31671366, 32070667
6 · The paper itself

Abstract

backgroundThe diagnosis of inflammatory bowel disease (IBD) and discrimination between the types of IBD are clinically important. IBD is associated with marked changes in the intestinal microbiota. Advances in next-generation sequencing (NGS) technology and the improved hospital bioinformatics analysis ability motivated us to develop a diagnostic method based on the gut microbiome.

resultsUsing a set of whole-genome sequencing (WGS) data from 349 human gut microbiota samples with two types of IBD and healthy controls, we assembled and aligned WGS short reads to obtain feature profiles of strains and genera. The genus and strain profiles were used for the 16S-based and WGS-based diagnostic modules construction respectively. We designed a novel feature selection procedure to select those case-specific features. With these features, we built discrimination models using different machine learning algorithms. The machine learning algorithm LightGBM outperformed other algorithms in this study and thus was chosen as the core algorithm. Specially, we identified two small sets of biomarkers (strains) separately for the WGS-based health vs IBD module and ulcerative colitis vs Crohn's disease module, which contributed to the optimization of model performance during pre-training. We released LightCUD as an IBD diagnostic program built with LightGBM. The high performance has been validated through five-fold cross-validation and using an independent test data set. LightCUD was implemented in Python and packaged free for installation with customized databases. With WGS data or 16S rRNA sequencing data of gut microbiome samples as the input, LightCUD can discriminate IBD from healthy controls with high accuracy and further identify the specific type of IBD. The executable program LightCUD was released in open source with instructions at the webpage http://cqb.pku.edu.cn/ZhuLab/LightCUD/ . The identified strain biomarkers could be used to study the critical factors for disease development and recommend treatments regarding changes in the gut microbial community.

conclusionsAs the first released human gut microbiome-based IBD diagnostic tool, LightCUD demonstrates a high-performance for both WGS and 16S sequencing data. The strains that either identify healthy controls from IBD patients or distinguish the specific type of IBD are expected to be clinically important to serve as biomarkers.

Indexed as

BiomarkerHuman gut microbiomeIBDMachine learning algorithm

Identifiers

PMID33468221
PMCPMC7816363

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

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.