CpG di-nucleotide Odds Ratio Measures Drive Unsupervised Clustering to Characterize the Andes Hantavirus
Bibliographic record
Abstract
Abstract Hantaviruses belong to the Bunyaviridae family with small mammals hosting them. Humans are infected either by inhaling virus-containing aerosols or through contact with animal droppings. Even if rodents host the pathogenic species and humans are dead-end hosts, they still get accidentally infected. The Andes Orthohantavirus (ANDV) seems to be the only species with documented person-to-person transmission. Hemorrhagic fever with renal syndrome (HFRS) and Hantavirus cardiopulmonary syndrome (HCPS) are both serious syndromes associated with hantavirus infections. For both syndromes, the mortality rate is near 40%. Decades of studies have already highlighted the CpG repression in RNA viruses, and both the estimation of the CpG odds ratio and the correlation with their genome polarity were dominant factors in figuring out the CpG bias. We conducted the differential analysis of the CpG odds ratio for all the orthohantaviruses on the full segmented genomes (L, M, S). The results suggested the statistical significance of the three groups. The “Small” genomes were more informative from the CpG odd ratio point of view. We calculated the CpG odds ratio for all the Orthohantaviruses within these segments and furthermore estimated the correlation coefficient with the relative coding sequences (CDS). Preliminary results first confirmed the CpG odds ratio as the lowest among all the nucleotides. Second, the Andes virus was highlighted as the one with the highest CpG odds ratio within CDS. The use of these two measures as features for unsupervised clustering algorithms has allowed us to identify four different sub-groups within the Orthohantaviridae family. The evidence is that the Andes Hantavirus exhibits a peculiar CpG odds ratio distribution, probably linked to its unique characteristic of passing from person to person.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".