HiTaxon: A hierarchical ensemble framework for taxonomic classification of short reads
Bibliographic record
Abstract
ABSTRACT Whole microbiome DNA and RNA sequencing (metagenomics and metatranscriptomics) are pivotal to determining functional roles within microbial communities. A key challenge in analysing these complex datasets, typically composed of tens of millions of short reads, is accurately classifying reads to their taxon of origin. Traditional reference-based short-read classification tools are compromised by reference database biases, leading to interest in classifiers leveraging machine learning (ML) algorithms. While ML classifiers have shown promise, they still lag reference-based tools in species-level classification. To address this performance gap, attention has turned to approaches that incorporate the hierarchical structure of taxonomic classifications, albeit with limited results. Here we introduce HiTaxon, a hierarchical framework for creating ensembles of reference-dependent and ML classifiers. HiTaxon facilitates data collection and processing, reference database construction and model training to streamline ensemble creation. We show that databases created by HiTaxon improve the species-level performance of reference-dependent classifiers, while reducing their computational overhead. Additionally, through exploring hierarchical methods for HiTaxon, we highlight that our custom hierarchical ML approach improves species-level classification relative to traditional strategies. Finally, we demonstrate the improved performance of our hierarchical ensemble over current state-of-the-art classifiers in species classification using datasets comprised of either simulated or experimentally-derived reads.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".