A New Fast Method for Detecting and Validating Horizontal Gene Transfer Events Using Phylogenetic Trees and Aggregation Functions
Bibliographic record
Abstract
This chapter presents a new algorithm, called horizontal gene transfer (HGT)-QFUNC, for detecting genomic regions that may be associated with complete HGT events, using phylogenetic trees and aggregation functions. It provides the details of the method for inferring complete HGT events. The chapter validates the obtained results with p-values calculated using a Monte Carlo approach. The advantage of the proposed algorithm is its quadratic time complexity on the number of considered species. The chapter then estimates the rates of complete HGT among prokaryotes comparing the results to the highly accurate, but much slower, HGT-Detection algorithm based on the calculation of bootstrap support of considered gene trees. It compares the results provided by HGT-QFUNC and HGT-Detection using simulated data, which will be representative of the prokaryotic landscape. The chapter finally shows that the proposed new functions and algorithm are capable of providing good detection rates for the highly probable HGT events.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".