GeneVis: Simulation and Visualization of Genetic Networks
Bibliographic record
Abstract
GeneVis simulates genetic networks and visualizes the process of this simulation interactively, providing a visual environment for exploring the dynamics of genetic regulatory networks. The visualization environment supports several representational modes, which include: an individual protein representation, a protein concentration representation, and a network structure representation. The individual protein representation shows the activities of the individual proteins. The protein concentration representation illustrates the relative spread and concentrations of the different proteins in the simulation. The network structure representation depicts the genetic network dependencies that are present in the simulation. GeneVis includes several interactive viewing tools. These include animated transitions from the individual protein representation to the protein concentration representation and from the individual protein representation to the network structure representation. Three types of lenses are used to provide different views within a representation: fuzzy lenses, base pair lenses, and the network structure ring lens. With a fuzzy lens an alternate representation can be viewed in a selected region. The base pair lenses allow users to reposition genes for better viewing or to minimize interference during the simulation. The ring lens provides detail-in-context viewing of individual levels in the genetic network structure representation.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.021 | 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".