An Improved Likelihood Ratio Test for Detecting Site-Specific Functional Divergence among Clades of Protein-Coding Genes
Bibliographic record
Abstract
Maximum likelihood codon substitution models have proven useful for studying when and how protein function evolves, but they have recently been criticized on a number of fronts. The strengths and weaknesses of such methods must therefore be identified and improved upon. Here, using simulations, we show that the Clade model C versus M1a test for functional divergence among clades is prone to false positives under simple evolutionary conditions. We then propose a new null model (M2a_rel) that better accounts for among-site variation in selective constraint. We show that the revised test has an improved false-positive rate and good power. Applying this test to previously analyzed data sets of primate ribonucleases and mammalian rhodopsins reveals that some conclusions may have been misled by the original method. The improved test should prove useful for identifying patterns of divergence in selective constraint among paralogous gene families and among orthologs from ecologically divergent species.
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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 | 0.016 | 0.059 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.007 | 0.010 |
| Insufficient payload (model declined to judge) | 0.002 | 0.003 |
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".