#36 · ML / AI methods

Statistical and neural methods for grouped microbiome data analysis

The common thread across these items is statistical methodology for microbiome community data, where sparsity, grouping structure, and computational scale complicate both inference and visualization. The F-informed multidimensional scaling method embeds the F-statistic into ordination so that grouped distributions are displayed alongside inferential content. StrataBionn applies a neural network classifier to microbial community state types and reports outperformance over nearest-neighbor and Random Forest baselines on vaginal and oral datasets. A random-effects network inference preprint targets computational bottlenecks and sparsity in microbiome interaction modeling, but its biological validation of inferred directions is incomplete and the preprint has been withdrawn. What is new is the pairing of inference-aware visualization with supervised learning, though the items do not establish biological validity of inferred network directions.