Citation

If TabFlux is useful in your work, please cite the software.

Antonielli, L., & Pucher, L. (2026). TabFlux: machine-learning classification of microbial community profiles (v1.6.0). Zenodo. https://doi.org/10.5281/zenodo.23041258

Each release gets its own version DOI: cite the one you ran. The concept DOI, 10.5281/zenodo.23041257, always resolves to the newest release. The repository carries a CITATION.cff file, so GitHub's "Cite this repository" button produces the entry in APA or BibTeX.

References

Cite the components a run relied on. The list follows the order of a run.

Framework

• Lang M, Binder M, Richter J, Schratz P, Pfisterer F, Coors S, Au Q, Casalicchio G, Kotthoff L, Bischl B (2019). mlr3: A modern object-oriented machine learning framework in R. Journal of Open Source Software 4(44):1903. https://doi.org/10.21105/joss.01903
• Binder M, Pfisterer F, Lang M, Schneider L, Kotthoff L, Bischl B (2021). mlr3pipelines – flexible machine learning pipelines in R. Journal of Machine Learning Research 22(184):1–7.
• Bischl B, Sonabend R, Kotthoff L, Lang M, eds. (2024). Applied Machine Learning Using mlr3 in R. CRC Press. https://mlr3book.mlr-org.com
• Allaire JJ, Teague C, Scheidegger C, Xie Y, Dervieux C, Woodhull G (2024). Quarto. https://doi.org/10.5281/zenodo.5960048
• Ushey K, Allaire JJ, Tang Y. reticulate: Interface to Python. R package. https://rstudio.github.io/reticulate/
• Bengtsson H (2021). A unifying framework for parallel and distributed processing in R using futures. The R Journal 13(2):208–227. https://doi.org/10.32614/RJ-2021-048

Input formats

• Callahan BJ, McMurdie PJ, Rosen MJ, Han AW, Johnson AJA, Holmes SP (2016). DADA2: high-resolution sample inference from Illumina amplicon data. Nature Methods 13:581–583. https://doi.org/10.1038/nmeth.3869 — the three-file layout mirrors its output.
• Blanco-Míguez A, Beghini F, Cumbo F, et al. (2023). Extending and improving metagenomic taxonomic profiling with uncharacterized species using MetaPhlAn 4. Nature Biotechnology 41:1633–1644. https://doi.org/10.1038/s41587-023-01688-w — the profiles behind the SGB level.

Depth normalisation

• Gloor GB, Macklaim JM, Pawlowsky-Glahn V, Egozcue JJ (2017). Microbiome datasets are compositional: and this is not optional. Frontiers in Microbiology 8:2224. https://doi.org/10.3389/fmicb.2017.02224
• Aitchison J (1986). The Statistical Analysis of Compositional Data. Chapman & Hall. — the centred log-ratio behind tss_clr.
• McMurdie PJ, Holmes S (2014). Waste not, want not: why rarefying microbiome data is inadmissible. PLoS Computational Biology 10(4):e1003531. https://doi.org/10.1371/journal.pcbi.1003531

Feature selection and class balancing

• Guyon I, Weston J, Barnhill S, Vapnik V (2002). Gene selection for cancer classification using support vector machines. Machine Learning 46:389–422. https://doi.org/10.1023/A:1012487302797 — recursive feature elimination.
• Breiman L, Friedman JH, Olshen RA, Stone CJ (1984). Classification and Regression Trees. Wadsworth. — the one-standard-error rule.
• Zawadzki Z, Kosinski M. FSelectorRcpp: Rcpp implementation of FSelector entropy-based feature selection algorithms. R package. — the information-gain filter.
• Chawla NV, Bowyer KW, Hall LO, Kegelmeyer WP (2002). SMOTE: synthetic minority over-sampling technique. Journal of Artificial Intelligence Research 16:321–357. https://doi.org/10.1613/jair.953
• Siriseriwan W. smotefamily: a collection of oversampling techniques for class imbalance problem based on SMOTE. R package.

Learners

• Breiman L (2001). Random forests. Machine Learning 45:5–32. https://doi.org/10.1023/A:1010933404324
• Wright MN, Ziegler A (2017). ranger: a fast implementation of random forests for high dimensional data in C++ and R. Journal of Statistical Software 77(1):1–17. https://doi.org/10.18637/jss.v077.i01
• Hollmann N, Müller S, Purucker L, Krishnakumar A, Körfer M, Hoo SB, Schirrmeister RT, Hutter F (2025). Accurate predictions on small data with a tabular foundation model. Nature 637:319–326. https://doi.org/10.1038/s41586-024-08328-6
• Prior Labs (2026). TabPFN-3.5 model card and licence. https://priorlabs.ai
• Friedman J, Hastie T, Tibshirani R (2010). Regularization paths for generalized linear models via coordinate descent. Journal of Statistical Software 33(1):1–22. https://doi.org/10.18637/jss.v033.i01 — glmnet, if picked.
• Chen T, Guestrin C (2016). XGBoost: a scalable tree boosting system. KDD 2016, 785–794. https://doi.org/10.1145/2939672.2939785 — xgboost, if picked.

Tuning

• Li L, Jamieson K, DeSalvo G, Rostamizadeh A, Talwalkar A (2018). Hyperband: a novel bandit-based approach to hyperparameter optimization. Journal of Machine Learning Research 18(185):1–52.
• Jamieson K, Talwalkar A (2016). Non-stochastic best arm identification and hyperparameter optimization. AISTATS 2016, PMLR 51:240–248. — successive halving.

Evaluation

• Varma S, Simon R (2006). Bias in error estimation when using cross-validation for model selection. BMC Bioinformatics 7:91. https://doi.org/10.1186/1471-2105-7-91
• Cawley GC, Talbot NLC (2010). On over-fitting in model selection and subsequent selection bias in performance evaluation. Journal of Machine Learning Research 11:2079–2107.
• Bengio Y, Grandvalet Y (2004). No unbiased estimator of the variance of K-fold cross-validation. Journal of Machine Learning Research 5:1089–1105.
• Brodersen KH, Ong CS, Stephan KE, Buhmann JM (2010). The balanced accuracy and its posterior distribution. ICPR 2010, 3121–3124. https://doi.org/10.1109/ICPR.2010.764
• Platt J (1999). Probabilistic outputs for support vector machines and comparisons to regularized likelihood methods. In: Advances in Large Margin Classifiers, MIT Press, 61–74. — the calibration step.
• Efron B, Tibshirani RJ (1993). An Introduction to the Bootstrap. Chapman & Hall. — the intervals on test sets.
• Pfisterer F, Wei S, Lang M. mlr3fairness: fairness auditing and debiasing for mlr3. R package. — the per-group fairness metrics.

Explanation

• Štrumbelj E, Kononenko I (2014). Explaining prediction models and individual predictions with feature contributions. Knowledge and Information Systems 41:647–665. https://doi.org/10.1007/s10115-013-0679-x
• Lundberg SM, Lee S-I (2017). A unified approach to interpreting model predictions. NeurIPS 30.
• Molnar C, Bischl B, Casalicchio G (2018). iml: an R package for interpretable machine learning. Journal of Open Source Software 3(26):786. https://doi.org/10.21105/joss.00786

Case study data

• Carlino N, Blanco-Míguez A, Punčochář M, et al. (2024). Unexplored microbial diversity from 2,500 food metagenomes and links with the human microbiome. Cell 187(20):5775–5795. https://doi.org/10.1016/j.cell.2024.07.039 — the curated Food Metagenomic Data (cFMD).

Acknowledgements

TabFlux grew out of the MICROSUPPRESS project (The role of wheat microbiomes in stress suppressiveness) at the AIT Austrian Institute of Technology, funded by the Austrian Science Fund (FWF), project P 36288.

Much of the road from a single notebook to v1.6.0 — the nested evaluation, the container, the cFMD module and this documentation — was travelled with Claude Code. Anthropic provided six months of Claude Max through their Open Source programme. Every line was reviewed by the authors.