On the way to building an integrated computational environment for the study of developmental patterns and genetic diseases
Bibliographic record
Abstract
Genetic diseases and developmental patterns should be studied on several levels: from macroscale (organs and tissues) to nanoscale (cells, genes, proteins). Due to the overwhelming complexity of the life science data, it is common that disparate data pieces are meticulously stored but never fully analyzed or correlated. We have begun to develop a novel methodology based on virtual reality techniques for the study of these phenomena. Our key approach to knowledge integration is a top-down mapping of data onto visual contexts. For each organism that we want to study, a structural model is created and used as a visual "wireframe" onto which other data types are superimposed in a top-down assembly. Data analysis tools, visual controls, and queries are enabled so that users can interactively explore data. Our visualization technology gives users an opportunity to map disparate data onto a common model, and search visually for hitherto unknown patterns and correlations contained within the data. It is our goal to eventually transform genomics research from measuring various data pieces analytically into a fully interactive exploration of combined data in a 4D immersive visual environment that best matches a researcher's intuition.
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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 | 0.005 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.006 | 0.009 |
| Open science | 0.005 | 0.006 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.017 | 0.004 |
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".