Understanding Histone H1 Binding Mechanism Through Model Comparison and FRAP Experiments
Bibliographic record
Abstract
I was honored to be the chair of the session “Life Sciences I” and represent Athabasca University at the 7th International Congress on Industrial and Applied Mathematics (ICIAM), 2011 (ICIAM 2011) that took place in Vancouver, BC. I had the opportunity to share part of my research during this session in the form of a presentation. The audience was receptive about the application of FRAP experiments, mathematical modeling and techniques in model comparison to analyze the binding mechanism of histone H1 to the chromatin structure. Since this is still work in progress there were some useful comments and suggestions about how to improve the analysis both theoretically and experimentally. I had the opportunity to receive meaningful comments from Dr. Adriana Dawes, a colleague from Ohio State University, USA. Also, I had the opportunity to had useful scientific conversations with Dr. Daniel Coombs, from University of British Columbia, who has worked in with similar experimental data that I have worked during my research. I expect to explore the suggestions made by my colleagues and think more deeply about the comments received during the Congress so that I can improve the analysis and design the future steps to follow in this research.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".