leADS: improved metabolic pathway inference based on active dataset subsampling
Bibliographic record
Abstract
Abstract Metabolic pathways are composed of reaction sequences catalyzed by enzymes. The set of reactions within and between cells comprises a reactome. Pathways and reactomes can be predicted from organismal or multi-organismal genomes using rule-based or machine learning methods. While machine learning methods overcome issues of probability and scale associated with rule-based methods, several complications remain that can degrade performance including inadequately labeled training data, missing feature information, and inherent imbalances in the distribution of pathways within a dataset. Here, we present leADS (mu l ti-label l e arning based on a ctive d ataset s ubsampling), a machine learning method, that uses subsampling to reduce the negative impact of training loss due to class imbalance. We demonstrate leADs performance using organismal and multi-organismal datasets in relation to other machine learning pathway prediction methods. Availability and implementation leADS is available under the GNU license at github.com/hallamlab/leADS. A wiki, including a tutorial, is available at github.com//hallamlab/leADS/wiki Contact shallam@mail.ubc.ca
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".