COMPUTATIONAL META-ANALYSIS OF CERVICAL CANCER USING AVAILABLE 16S RRNA NGS DATA
Bibliographic record
Abstract
Cervical cancer is one of the most frequently occurring and deadliest gynaecological cancer which develops in cervical cells. Since it develops in tissues lining the internal organs it is a Carcinoma. Human papilloma virus infection is found to top the list of carcinogenic factors. Overexpression of certain proteins due to HPV integration in host body over a time can result in carcinoma. However vaginal microbiota plays a key role in development, persistence and progression of infections leading to diseases such as cervical cancer. Some recent studies have revealed potential roles of microbiome in cervicovaginal diseases. Thus a comparative metagenomic study among such samples can uncover microbial diversities present in these samples. Due to presence of highly conserved regions as well as hyper variable regions 16s rRNA gene sequence is selected for identification and classification of bacterial diversity. For the purpose of metagenomic analysis 16s rRNA gene sequences were analysed using QIIME pipeline.
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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 | 0.004 | 0.011 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.010 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".