Classification of the short‐chain dehydrogenase/reductase superfamily using hidden Markov models
Bibliographic record
Abstract
The short-chain dehydrogenase/reductase (SDR) superfamily now has over 47 000 members, most of which are distantly related, with typically 20-30% residue identity in pairwise comparisons, making it difficult to obtain an overview of this superfamily. We have therefore developed a family classification system, based upon hidden Markov models (HMMs). To this end, we have identified 314 SDR families, encompassing about 31,900 members. In addition, about 9700 SDR forms belong to families with too few members at present to establish valid HMMs. In the human genome, we find 47 SDR families, corresponding to 82 genes. Thirteen families are present in all three domains (Eukaryota, Bacteria, and Archaea), and are hence expected to catalyze fundamental metabolic processes. The majority of these enzymes are of the 'extended' type, in agreement with earlier findings. About half of the SDR families are only found among bacteria, where the 'classical' SDR type is most prominent. The HMM-based classification is used as a basis for a sustainable and expandable nomenclature system.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".