ArticleCell reports methods2024
Scalable log-ratio lasso regression for enhanced microbial feature selection with FLORAL.
Article in Cell reports methods, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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5 citing papers in PubMed.
- Microbiome legacy influences necrosis formation in Diplodia sapinea-infected Scots pine shoots.Environmental microbiome · 2026Article
- DynaBiome: interpretable unsupervised learning of gut microbiome dysbiosis via temporal deep models.BMC bioinformatics · 2026Article
- Interpretable machine learning uncovers core seminal microbial signatures in idiopathic oligoasthenospermia.Frontiers in cellular and infection microbiology · 2026Article
- Correlating High-dimensional longitudinal microbial features with time-varying outcomes with FLORAL.bioRxiv : the preprint server for biology · 2025Article
- Longitudinal Microbiome-based Interpretable Machine Learning for Identification of Time-Varying Biomarkers in Early Prediction of Disease Outcomes.bioRxiv : the preprint server for biology · 2024Article
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Abstract
Identifying predictive biomarkers of patient outcomes from high-throughput microbiome data is of high interest, while existing computational methods do not satisfactorily account for complex survival endpoints, longitudinal samples, and taxa-specific sequencing biases. We present FLORAL, an open-source tool to perform scalable log-ratio lasso regression and microbial feature selection for continuous, binary, time-to-event, and competing risk outcomes, with compatibility for longitudinal microbiome data as time-dependent covariates. The proposed method adapts the augmented Lagrangian algorithm for a zero-sum constraint optimization problem while enabling a two-stage screening process for enhanced false-positive control. In extensive simulation and real-data analyses, FLORAL achieved consistently better false-positive control compared to other lasso-based approaches and better sensitivity over popular differential abundance testing methods for datasets with smaller sample sizes. In a survival analysis of allogeneic hematopoietic cell transplant recipients, FLORAL demonstrated considerable improvement in microbial feature selection by utilizing longitudinal microbiome data over solely using baseline microbiome data.
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