Genomic scale sorting and characterization of MITEs in the entangled mobilome
Bibliographic record
Abstract
M inverted repeat transposable elements (MITEs) are interesting transposable elements (TEs) because of their high copy numbers and mysterious identifies. Despite their low DNA content percentage in a genome, their numerous copies can disturb genomic stability and cause important genetic variations. Historically, MITE families were often discovered individually, a practice cannot keep pace with the large scale genome sequencing. Automated genome wide discovery of MITE families and their characterization are desirable. We developed a whole pipeline from MITE discovery to detailed analyses of MITEs at genomic scales. Using the pipeline, we performed in depth analyses of the MITE families in multiple recently release crop genomes. These analyses revealed the diversity of MITE families and their evolution in the host genome. We have also predicted the transposases that may be responsible for the mobilization of some MITE families. These MITE families can potentially be used as genetic markers for the improvement of these crop species. Guojun Yang, J Data Mining Genomics Proteomics 2013, 4:5 http://dx.doi.org/10.4172/2153-0602.S1.004
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".