UniCoracle: Automated hierarchical Feature Selection via Bottom-Up Propagation and Top-Down Skimming using the UniCorP algorithm and the Coracle machine-learning framework

Clicks: 1
ID: 320336
2026
Free PDF
Article Quality & Performance Metrics
Overall Quality
Not rated
Combines reader engagement with the AI quality analysis. This article has not been analysed, so there is no overall score — reader engagement is measured and shown alongside.
AI Quality Assessment
Not analyzed
Readership in this journal

Ranked #798 of 821 articles by views in BMC Bioinformatics

Most read Least read

Bar heights use a square-root scale. Only the 120 most-read articles are drawn; the journal has 821 in total.

Mint this article as an NFT
Not yet minted

Create a permanent, verifiable on-chain record of this article on the Scimatic Network. The NFT is held in your Journament account, and you can withdraw it to your own wallet at any time.

5 SUSD one-off · no wallet required
Sign in to mint
Abstract
Identifying meaningful associations between microbial communities and measured physiological or environmental variables becomes increasingly complex and computationally demanding given the continuous growth of microbiome datasets. The Coracle machine learning (ML) framework was recently developed to address this by integrating multiple data transformations, feature selection techniques, and ML models to yield condensed lists of features that align to target variables of interest. Further, we recently developed the UniCorP feature aggregation algorithm to identify uniquely correlated features (UNICORNs) based on the UniCor metric that iteratively enrich each taxonomic level in an automated bottom-up approach. Here we present UniCoracle, a fully automated analytical framework that integrates UniCorP's bottom-up propagation approach with a subsequent and newly developed top-down skimming (TDS) strategy, implemented with the Coracle ML framework. This combined approach leverages the inherent taxonomic structure of microbiome community data (e.g., ASVs derived from 16S rRNA gene amplicon sequencing) to maintain predictive stability, reduce computational runtime, and identify biologically meaningful taxonomic associations. We compare the original, non-hierarchical Coracle with the TDS Coracle method and the UniCoracle approach. Evaluations across the tested datasets show that UniCoracle achieves competitive or improved predictive performance relative to both Coracle's multi-step and the TDS-based Coracle implementations. Our results demonstrate UniCoracle's improvements in predictive accuracy over both Coracle's multi-step and the TDS Coracle methods. UniCoracle provides full control over feature set size and runtime, offering a streamlined and user-friendly framework for biological hypothesis generation. It identifies features (e.g., bacterial taxa) at the lowest (most specific) hierarchical level (e.g., ASV or species within a taxonomic hierarchy) that are associated with continuous target variables. Availability: UniCoracle is freely accessible via a dedicated web server at micportal.org. The source code is open-source and available on GitHub at github.com/SebastianStaab/UniCoracle.git and Zenodo at https://doi.org/10.5281/zenodo.19050205. Supplementary information: Example datasets and a tutorial are provided on the web server: micportal.org.
Reference Key
openalex_W7167809224 Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Sebastian Staab, Anny Cárdenas, Raquel S Peixoto, Falk Schreiber, Christian R Voolstra
Journal BMC Bioinformatics
Year 2026
DOI
10.1093/bioinformatics/btag507
URL
https://doi.org/10.1093/bioinformatics/btag507
Keywords Keywords not found

Citations

No citations found. To add a citation, contact the admin at info@scimatic.org

Comments

No comments yet. Be the first to comment on this article.