Mirage: A phylogenetic mixture model to reconstruct gene-content evolutionary history using a realistic evolutionary rate model
Bibliographic record
Abstract
Abstract Reconstruction of gene-content evolutionary history is an essential approach for understanding how complex biological systems have been organized. However, the existing gene-content evolutionary models cannot formulate complex and heterogeneous gene gain/loss processes, which reflect diverse evolutionary events and greatly depend on gene families. In this study, we developed Mirage ( MI xture model with a R ealistic evolutionary rate model for A ncestral G enome E stimation), which allows different gene families to have flexible gene gain/loss rates, but reasonably limits the number of parameters to be estimated by the expectation-maximization algorithm. Simulation analysis showed that Mirage can accurately estimate complex and heterogeneous gene gain/loss rates and reconstruct gene-content evolutionary history. Application to empirical datasets demonstrated that our evolutionary model better fits genome data from various taxonomic groups than other models. Using Mirage, we revealed that gene families of metabolic function-related gene families displayed frequent gene gains and losses in all taxa investigated. The source code of Mirage is freely available at https://github.com/fukunagatsu/Mirage .
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| 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".