A Whole Genome Study and Identification of Specific Carcinogenic Regions of the Human Papilloma Viruses
Bibliographic record
Abstract
In this article, we undertake a study of the evolution of human papillomaviruses (HPV), whose potential to cause cervical cancer is well known. First, we found that the existing HPV groups are monophyletic and that the high risk of carcinogenicity taxa are usually clustered together. Then, we present a new algorithm for analyzing the information content of multiple sequence alignments in relation to epidemiologic carcinogenicity data to identify regions that would warrant additional experimental analyses. The new algorithm is based on a sliding window procedure and a p-value computation to identify genomic regions that are specific to HPVs causing disease. Examination of the genomes of 83 HPVs allowed us to identify specific regions that might be influenced by insertions, by deletions, or simply by mutations, and that may be of interest for further analyses. Supplementary Material is provided (see online Supplementary Material at www.libertonline.com ).
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 teacher head, 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".