ArticleFrontiers in genetics2020
A t-SNE Based Classification Approach to Compositional Microbiome Data.
Article in Frontiers in genetics, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 27 papers.
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Who cites it
27 citing papers in PubMed.
- Integrating evolutionary and compositional features with ML and DL for robust and interpretable druggable protein prediction.Journal of computer-aided molecular design · 2026Article
- AI-empowered human microbiome research.Gut · 2026Review
- Article
- Complex correlations between mitochondrial DNA variants and gut microbiome in major depressive disorder: a genome-wide association analysis.BMC psychiatry · 2026Article
- Microbiota-host interaction in colorectal cancer: emerging computational technology, multi-omics integration, and mechanisms.Cancer biology & medicine · 2026Review
- Credible inferences in microbiome research: ensuring rigour, reproducibility and relevance in the era of AI.Nature reviews. Gastroenterology & hepatology · 2025Review
- EEG-Based Deep Learning Model for Hyper-Acute Large Vessel Occlusion Stroke Detection in Mice.CNS neuroscience & therapeutics · 2025Article
- Correlations between gut microbiota and serum metabolomics in patients with neurogenic rosacea.BMC microbiology · 2025Article
- Peripheral Single-Cell Immune Characteristics Contribute to the Diagnosis of Alzheimer's Disease and Dementia With Lewy Bodies.CNS neuroscience & therapeutics · 2025Article
- Combination ATR-FTIR with Multiple Classification Algorithms for Authentication of the Four Medicinal Plants fromSensors (Basel, Switzerland) · 2024Article
- Exploring and exploiting the rice phytobiome to tackle climate change challenges.Plant communications · 2024Review
- AFITbin: a metagenomic contig binning method using aggregate l-mer frequency based on initial and terminal nucleotides.BMC bioinformatics · 2024Article
- Effect of Altitude Gradients on the Spatial Distribution Mechanism of Soil Bacteria in Temperate Deciduous Broad-Leaved Forests.Microorganisms · 2024Article
- The Rapid Non-Destructive Differentiation of Different Varieties of Rice by Fluorescence Hyperspectral Technology Combined with Machine Learning.Molecules (Basel, Switzerland) · 2024Article
- Mock community taxonomic classification performance of publicly available shotgun metagenomics pipelines.Scientific data · 2024Article
- Advanced computational tools, artificial intelligence and machine-learning approaches in gut microbiota and biomarker identification.Frontiers in medical technology · 2024Review
- Revolutionizing Medicinal Chemistry: The Application of Artificial Intelligence (AI) in Early Drug Discovery.Pharmaceuticals (Basel, Switzerland) · 2023Review
- A cross entropy test allows quantitative statistical comparison of t-SNE and UMAP representations.Cell reports methods · 2023Article
- Machine learning for data integration in human gut microbiome.Microbial cell factories · 2022Review
- Machine learning and deep learning applications in microbiome research.ISME communications · 2022Review
Corrections and comments
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
As a data-driven dimensionality reduction and visualization tool, t-distributed stochastic neighborhood embedding (t-SNE) has been successfully applied to a variety of fields. In recent years, it has also received increasing attention for classification and regression analysis. This study presented a t-SNE based classification approach for compositional microbiome data, which enabled us to build classifiers and classify new samples in the reduced dimensional space produced by t-SNE. The Aitchison distance was employed to modify the conditional probabilities in t-SNE to account for the compositionality of microbiome data. To classify a new sample, its low-dimensional features were obtained as the weighted mean vector of its nearest neighbors in the training set. Using the low-dimensional features as input, three commonly used machine learning algorithms, logistic regression (LR), support vector machine (SVM), and decision tree (DT) were considered for classification tasks in this study. The proposed approach was applied to two disease-associated microbiome datasets, achieving better classification performance compared with the classifiers built in the original high-dimensional space. The analytic results also showed that t-SNE with Aitchison distance led to improvement of classification accuracy in both datasets. In conclusion, we have developed a t-SNE based classification approach that is suitable for compositional microbiome data and may also serve as a baseline for more complex classification models.
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