ArticleBioMed research international2018
Metagenomics Biomarkers Selected for Prediction of Three Different Diseases in Chinese Population.
Article in BioMed research international, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 papers, 1 of them a synthesis that pooled it.
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Who cites it
22 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Host phenotype classification from human microbiome data is mainly driven by the presence of microbial taxa.PLoS computational biology · 2022Pooled it
- Coenocline Simulation of Microbiome Samples: A Biologically Mechanistic Framework for Generating Ecologically Realistic Synthetic Datasets to Support Classification Method Evaluation.Pathogens (Basel, Switzerland) · 2026Article
- Emerging Therapeutic Approaches for Modulating the Intestinal Microbiota.Pharmaceutics · 2026Review
- Decoding the reproductive microbiome: enabling clinical and biological insights through machine and deep learning.Frontiers in endocrinology · 2026Review
- A data-driven universal gut microbiome health assessment: a machine learning framework trained on large metagenomic data.Frontiers in microbiology · 2026Article
- MMETHANE: interpretable AI for predicting host status from microbial composition and metabolomics data.Microbiome · 2025Article
- Exploring the role of normalization and feature selection in microbiome disease classification pipelines.GigaScience · 2025Article
- A permutable MLP-like architecture for disease prediction from gut metagenomic data.BMC bioinformatics · 2024Article
- Article
- Machine learning-based approaches for cancer prediction using microbiome data.Scientific reports · 2023Article
- Overview of data preprocessing for machine learning applications in human microbiome research.Frontiers in microbiology · 2023Review
- Predicting environmental stressor levels with machine learning: a comparison between amplicon sequencing, metagenomics, and total RNA sequencing based on taxonomically assigned data.Frontiers in microbiology · 2023Article
- Artificial Intelligence in Rheumatoid Arthritis: Current Status and Future Perspectives: A State-of-the-Art Review.Rheumatology and therapy · 2022Review
- BiGAMi: Bi-Objective Genetic Algorithm Fitness Function for Feature Selection on Microbiome Datasets.Methods and protocols · 2022Article
- Prediction of the occurrence of calcium oxalate kidney stones based on clinical and gut microbiota characteristics.World journal of urology · 2022Article
- It takes guts to learn: machine learning techniques for disease detection from the gut microbiome.Emerging topics in life sciences · 2021Review
- Applications of Machine Learning in Human Microbiome Studies: A Review on Feature Selection, Biomarker Identification, Disease Prediction and Treatment.Frontiers in microbiology · 2021Review
- A systematic review of the applications of artificial intelligence and machine learning in autoimmune diseases.NPJ digital medicine · 2020Review
- Establishment and evaluation of prediction model for multiple disease classification based on gut microbial data.Scientific reports · 2019Article
- A Review and Tutorial of Machine Learning Methods for Microbiome Host Trait Prediction.Frontiers in genetics · 2019Review
Corrections and comments
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Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The dysbiosis of human microbiome has been proven to be associated with the development of many human diseases. Metagenome sequencing emerges as a powerful tool to investigate the effects of microbiome on diseases. Identification of human gut microbiome markers associated with abnormal phenotypes may facilitate feature selection for multiclass classification. Compared with binary classifiers, multiclass classification models deploy more complex discriminative patterns. Here, we developed a pipeline to address the challenging characterization of multilabel samples. In this study, a total of 300 biomarkers were selected from the microbiome of 806 Chinese individuals (383 controls, 170 with type 2 diabetes, 130 with rheumatoid arthritis, and 123 with liver cirrhosis), and then logistic regression prediction algorithm was applied to those markers as the model intrinsic features. The estimated model produced an
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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.