Pathline: A Tool For Comparative Functional Genomics
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract Biologists pioneering the new field of comparative functional genomics attempt to infer the mechanisms of gene regulation by looking for similarities and differences of gene activity over time across multiple species. They use three kinds of data: functional data such as gene activity measurements, pathway data that represent a series of reactions within a cellular process, and phylogenetic relationship data that describe the relatedness of species. No existing visualization tool can visually encode the biologically interesting relationships between multiple pathways, multiple genes, and multiple species. We tackle the challenge of visualizing all aspects of this comparative functional genomics dataset with a new interactive tool called Pathline. In addition to the overall characterization of the problem and design of Pathline, our contributions include two new visual encoding techniques. One is a new method for linearizing metabolic pathways that provides appropriate topological information and supports the comparison of quantitative data along the pathway. The second is the curvemap view, a depiction of time series data for comparison of gene activity and metabolite levels across multiple species. Pathline was developed in close collaboration with a team of genomic scientists. We validate our approach with case studies of the biologists’ use of Pathline and report on how they use the tool to confirm existing findings and to discover new scientific insights.
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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 it