Bibliographic record
Abstract
Comparative analysis in genomics involves comparing two or more genomes to identify conserved genetic information. These duplicated regions can indicate shared ancestry and can shed light on an organism's internal functions and evolutionary history. Due to rapid advances in sequencing technology, high-resolution genome data is now available for a wide range of species, and comparative analysis of this data can provide insights that can be applied in medicine, plant breeding, and many other areas. Comparative genomics is a strongly interactive task, and visualizing the location, size, and orientation of conserved regions can assist researchers by supporting critical activities of interpretation and judgment. However, visualization tools for the analysis of conserved regions have not kept pace with the increasing availability of genomic information and the new ways in which this data is being used by biological researchers. To address this gap, we gathered requirements for interactive exploration from three groups of expert genomic scientists, and developed a web-based tool called SynVisio with novel interaction techniques and visual representations to meet those needs. Our tool supports multi-resolution analysis, provides interactive filtering as researchers move deeper into the genome, supports revisitation to specific interface configurations, and enables loosely-coupled collaboration over the genomic data. An evaluation of the system with five researchers from three expert groups provides evidence about the success of our system's novel techniques for supporting interactive exploration of genomic conservation.
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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 | 0.003 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 0.002 |
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".