Automatic detection of rate change in large data sets with an unsupervised approach: the case of influenza viruses
Bibliographic record
Abstract
Influenza viruses evolve at such a high rate that vaccine recommendations need to be changed, but not quite on a regular basis. This observation suggests that the rate of evolution of these viruses is not constant through time, which begs the question as to when such rate changes occur, if they do so independently of the host in which they circulate and (or) independently of their subtype. To address these outstanding questions, we introduce a novel heuristics, Mclust*, that is based on a two-tier clustering approach in a phylogenetic context to estimate (i) absolute rates of evolution and (ii) when rate change occurs. We employ the novel approach to compare the two influenza surface proteins, hemagglutinin and neuraminidase, that circulated in avian, human, and swine hosts between 1960 and 2014 in two subtypes: H3N2 and H1N1. We show that the algorithm performs well in most conditions, accounting for phylogenetic uncertainty by means of bootstrapping and scales up to analyze very large data sets. Our results show that our approach is robust to the time-dependent artifact of rate estimation, and confirm pervasive punctuated evolution across hosts and subtypes. As such, the novel approach can potentially detect when vaccine composition needs to be updated.
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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 | 0.007 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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".