ArticleFrontiers in immunology2021
Microbiome Preprocessing Machine Learning Pipeline.
Article in Frontiers in immunology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.
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Who cites it
15 citing papers in PubMed.
- Role of microbial metabolites in the pathogenesis of hypertensive disorders of pregnancy: From short‑chain fatty acids to tryptophan metabolites (Review).Molecular medicine reports · 2026Review
- Decoding the cancer microbiome: multi-omics, AI, and translational opportunities.Genome biology · 2026Review
- Integrating gut microbiota and violence exposure metrics to classify psychological distress in middle-aged adults.Communications medicine · 2026Article
- Early pregnancy is characterized by a significant decrease in the diversity of the oral microbiome and strong associations with lifestyle and conception method.Journal of oral microbiology · 2026Article
- A New Unsupervised Machine Learning Method for Metabarcoding Analysis Based on Selected Mock Community Data.Computational and structural biotechnology journal · 2026Article
- GIMIC: smoothed graph-image representation of microbiome samples induce an optimal distance.Genome biology · 2025Article
- Microbiome-based prediction of allogeneic hematopoietic stem cell transplantation outcome.Genome medicine · 2025Article
- mi-Mic: a novel multi-layer statistical test for microbiota-disease associations.Genome biology · 2024Article
- The Cross-talk Between Intestinal Microbiota and MDSCs Fuels Colitis-associated Cancer Development.Cancer research communications · 2024Article
- Gut microbiome-metabolome interactions predict host condition.Microbiome · 2024Article
- A GLM-based zero-inflated generalized Poisson factor model for analyzing microbiome data.Frontiers in microbiology · 2024Article
- Compositionality, sparsity, spurious heterogeneity, and other data-driven challenges for machine learning algorithms within plant microbiome studies.Current opinion in plant biology · 2023Review
- From the nose to the lungs: the intricate journey of airborne pathogens amid commensal bacteria.American journal of physiology. Cell physiology · 2022Review
- Applications of Machine Learning Models to Predict and Prevent Obesity: A Mini-Review.Frontiers in nutrition · 2022Review
- Ordering taxa in image convolution networks improves microbiome-based machine learning accuracy.Gut microbesArticle
Corrections and comments
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background: 16S sequencing results are often used for Machine Learning (ML) tasks. 16S gene sequences are represented as feature counts, which are associated with taxonomic representation. Raw feature counts may not be the optimal representation for ML. Methods: We checked multiple preprocessing steps and tested the optimal combination for 16S sequencing-based classification tasks. We computed the contribution of each step to the accuracy as measured by the Area Under Curve (AUC) of the classification. Results: We show that the log of the feature counts is much more informative than the relative counts. We further show that merging features associated with the same taxonomy at a given level, through a dimension reduction step for each group of bacteria improves the AUC. Finally, we show that z-scoring has a very limited effect on the results. Conclusions: The prepossessing of microbiome 16S data is crucial for optimal microbiome based Machine Learning. These preprocessing steps are integrated into the MIPMLP - Microbiome Preprocessing Machine Learning Pipeline, which is available as a stand-alone version at: https://github.com/louzounlab/microbiome/tree/master/Preprocess or as a service at http://mip-mlp.math.biu.ac.il/Home Both contain the code, and standard test sets.
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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.