ArticleScientific reports2019
Establishment and evaluation of prediction model for multiple disease classification based on gut microbial data.
Article in Scientific reports, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers, 2 of them syntheses that pooled 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.
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.
Who cites it
17 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- The role of machine learning in developing non-magnetic resonance imaging based biomarkers for multiple sclerosis: a systematic review.BMC medical informatics and decision making · 2022Pooled it
- Host phenotype classification from human microbiome data is mainly driven by the presence of microbial taxa.PLoS computational biology · 2022Pooled it
- Instance-based transfer learning enables cross-cohort early detection of colorectal cancer.NPJ biofilms and microbiomes · 2026Article
- Comparative metagenomics using pan-metagenomic graphs.bioRxiv : the preprint server for biology · 2025Article
- The Gut Microbiome in Anxiety Disorders.Current psychiatry reports · 2025Review
- A review of machine learning methods for cancer characterization from microbiome data.NPJ precision oncology · 2024Review
- Machine Learning Analysis of RNA-seq Data for Diagnostic and Prognostic Prediction of Colon Cancer.Sensors (Basel, Switzerland) · 2023Article
- Review
- Predictive association of gut microbiome and NLR in anemic low middle-income population of Odisha- a cross-sectional study.Frontiers in nutrition · 2023Article
- Predicting microbe organisms using data of living micro forms of life and hybrid microbes classifier.PloS one · 2023Article
- BiGAMi: Bi-Objective Genetic Algorithm Fitness Function for Feature Selection on Microbiome Datasets.Methods and protocols · 2022Article
- Benchmark of Data Processing Methods and Machine Learning Models for Gut Microbiome-Based Diagnosis of Inflammatory Bowel Disease.Frontiers in genetics · 2022Article
- Vaginal microbiome as a tool for prediction of chorioamnionitis in preterm labor: a pilot study.Scientific reports · 2021Observational
- Microbiome meta-analysis and cross-disease comparison enabled by the SIAMCAT machine learning toolbox.Genome biology · 2021Article
- Recent progress in the treatment of non-systemic juvenile idiopathic arthritis.Faculty reviews · 2021Review
- Identification of Potential Oral Microbial Biomarkers for the Diagnosis of Periodontitis.Journal of clinical medicine · 2020Article
- Interpretable and accurate prediction models for metagenomics data.GigaScience · 2020Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Diseases prediction has been performed by machine learning approaches with various biological data. One of the representative data is the gut microbial community, which interacts with the host's immune system. The abundance of a few microorganisms has been used as markers to predict diverse diseases. In this study, we hypothesized that multi-classification using machine learning approach could distinguish the gut microbiome from following six diseases: multiple sclerosis, juvenile idiopathic arthritis, myalgic encephalomyelitis/chronic fatigue syndrome, acquired immune deficiency syndrome, stroke and colorectal cancer. We used the abundance of microorganisms at five taxonomy levels as features in 696 samples collected from different studies to establish the best prediction model. We built classification models based on four multi-class classifiers and two feature selection methods including a forward selection and a backward elimination. As a result, we found that the performance of classification is improved as we use the lower taxonomy levels of features; the highest performance was observed at the genus level. Among four classifiers, LogitBoost-based prediction model outperformed other classifiers. Also, we suggested the optimal feature subsets at the genus-level obtained by backward elimination. We believe the selected feature subsets could be used as markers to distinguish various diseases simultaneously. The finding in this study suggests the potential use of selected features for the diagnosis of several diseases.
Indexed as
Identifiers
What OpenQuestion holds
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.