Projecting 2D gene expression data into 3D and 4D space
Bibliographic record
Abstract
Video games typically generate virtual 3D objects by texture mapping an image onto a 3D polygonal frame. The feeling of movement is then achieved by mathematically simulating camera movement relative to the polygonal frame. We have built customized scripts that adapt video game authoring software to texture mapping images of gene expression data onto b-spline based embryo models. This approach, known as UV mapping, associates two-dimensional (U and V) coordinates within images to the three dimensions (X, Y, and Z) of a b-spline model. B-spline model frameworks were built either from confocal data or de novo extracted from 2D images, once again using video game authoring approaches. This system was then used to build 3D models of 182 genes expressed in developing Xenopus embryos and to implement these in a web-accessible database. Models can be viewed via simple Internet browsers and utilize openGL hardware acceleration via a Shockwave plugin. Not only does this database display static data in a dynamic and scalable manner, the UV mapping system also serves as a method to align different images to a common framework, an approach that may make high-throughput automated comparisons of gene expression patterns possible. Finally, video game systems also have elegant methods for handling movement, allowing biomechanical algorithms to drive the animation of models. With further development, these biomechanical techniques offer practical methods for generating virtual embryos that recapitulate morphogenesis.
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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 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 0.003 |
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".