A Diffusion-approximation Approach to Model Self-organization of Nuclear Proteins
Bibliographic record
Abstract
The presentation was part of the Symposium “From individual to continuous approaches in biological modeling” at the MITACS 2009 Annual Conference. I included another topic to my presentation, and therefore the title of the presentation changed to: “Describing the motion of cellular proteins at individual and population levels”. The presentation aimed at describing two modeling approaches (population- and individual-based modeling) when using data from two fluorescence microscopy techniques, namely Fluorescence Recovery After Photobleaching (FRAP) and Single Particle Tracking (SPT). To illustrate the population-based modeling using FRAP data, a model to describe the self-organization of nuclear proteins was presented, and to illustrate the individual-based modeling using SPT data, a test for a correlated random walk was presented. The presentation of these two approaches brought about interesting questions regarding the use of one to supplement the other. A productive research meeting took place during the Symposium with Dr. Raibatak Das (organizer of the Symposium and postdoctoral fellow at UBC), Dr. Raibatak Das (a participant at the Symposium and professor at UBC), Jennifer Morrison (a presenter at the Symposium and PhD student at UBC), and Dr. Gerda de Vries (presenter at the Conference and professor at UofA) and a follow up meeting for common research interest a collaboration is scheduled for middle August 2009 at the University of Alberta. Also, I was a poster judge at the Conference poster competition.
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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 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".