Gene families as soft cliques with backbones: Amborellacontrasted with other flowering plants
Bibliographic record
Abstract
BACKGROUND: Chaining is a major problem in constructing gene families. RESULTS: We define a new kind of cluster on graphs with strong and weak edges: soft cliques with backbones (SCWiB). This differs from other definitions in how it controls the "chaining effect", by ensuring clusters satisfy a tolerant edge density criterion that takes into account cluster size. We implement algorithms for decomposing a graph of similarities into SCWiBs. We compare examples of output from SCWiB and the Markov Cluster Algorithm (MCL), and also compare some curated Arabidopsis thaliana gene families with the results of automatic clustering. We apply our method to 44 published angiosperm genomes with annotation, and discover that Amborella trichopoda is distinct from all the others in having substantially and systematically smaller proportions of moderate- and large-size gene families. CONCLUSIONS: We offer several possible evolutionary explanations for this result.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".