Pathway Analyst Automated Metabolic Pathway Prediction
Bibliographic record
Abstract
Metabolic pathways are crucial to our understanding of biology. The speed at which new organisms are being sequenced is outstripping our ability to experimentally determine their metabolic pathway information. In recent years several initiatives have been successful in automating the annotations of individual proteins in these organisms, either experimentally or by prediction. However, to leverage the success of metabolic pathways we need to automate their identification in our rapidly growing list of sequenced organisms. We present a prototype system for predicting the catalysts of important reactions and for organizing the predicted catalysts and reactions into previously defined metabolic pathways. We compare a variety of predictors that incorporate sequence similarity (BLAST), hidden Markov models (HMM) and Support Vector Machines (SVM). We found that there is an advantage to using different predictors for different reactions. We validate our prototype on 10 metabolic pathways across 13 organisms for which we obtained a cross-validation precision of 71.5% and recall of 91.5% in predicting the catalyst proteins of all reactions.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.031 | 0.007 |
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".