ReviewComputational and structural biotechnology journal2021
Towards multi-label classification: Next step of machine learning for microbiome research.
Review in Computational and structural biotechnology journal, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
What it found
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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.
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Who cites it
8 citing papers in PubMed, 27 citations in OpenAlex.
- Single-label and multi-label classification for disease recognition with special consideration of comorbidities.BMC medical research methodology · 2026Article
- Large-scale classification of metagenomic samples: a comparative analysis of classical machine learning techniques vs a novel brain-inspired hyperdimensional computing approach.bioRxiv : the preprint server for biology · 2025Article
- PhyloMix: enhancing microbiome-trait association prediction through phylogeny-mixing augmentation.Bioinformatics (Oxford, England) · 2025Article
- Advanced computational tools, artificial intelligence and machine-learning approaches in gut microbiota and biomarker identification.Frontiers in medical technology · 2024Review
- Comprehensive Assessment of 16S rRNA Gene Amplicon Sequencing for Microbiome Profiling across Multiple Habitats.Microbiology spectrum · 2023Article
- Overview of data preprocessing for machine learning applications in human microbiome research.Frontiers in microbiology · 2023Review
- An Improved Diagnostic of theDiagnostics (Basel, Switzerland) · 2022Article
- Parallel-Meta Suite: Interactive and rapid microbiome data analysis on multiple platforms.iMeta · 2022Article
Corrections and comments
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Authors and funding
6 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Machine learning (ML) has been widely used in microbiome research for biomarker selection and disease prediction. By training microbial profiles of samples from patients and healthy controls, ML classifiers constructs data models by community features that highly correlated with the target diseases, so as to determine the status of new samples. To clearly understand the host-microbe interaction of specific diseases, previous studies always focused on well-designed cohorts, in which each sample was exactly labeled by a single status type. However, in fact an individual may be associated with multiple diseases simultaneously, which introduce additional variations on microbial patterns that interferes the status detection. More importantly, comorbidities or complications can be missed by regular ML models, limiting the practical application of microbiome techniques. In this review, we summarize the typical ML approaches of single-label classification for microbiome research, and demonstrate their limitations in multi-label disease detection using a real dataset. Then we prospect a further step of ML towards multi-label classification that potentially solves the aforementioned problem, including a series of promising strategies and key technical issues for applying multi-label classification in microbiome-based studies.
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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.