Antigen encoding theory: Publication-ready version with documentation
Bibliographic record
Abstract
Code to reproduce the theoretical results of Sooraj R. Achar#, François X. P. Bourassa#, Thomas J. Rademaker#, Angela Lee, Taisuke Kondo, Emanuel Salazar-Cavazos, John S. Davies, Naomi Taylor, Paul François, and Grégoire Altan-Bonnet. "Universal antigen encoding of T cell activation from high dimensional cytokine data", submitted, 2021. (#: these authors contributed equally) The following main theoretical analyses are included in this repository: Modelling cytokine dynamics in latent space Generating cytokine data with reconstruction from the latent space Computing the channel capacity of cytokine dynamics for antigen quality. All cytokine data necessary to run the code is included in the Github repository. Also included are neural network weights that produce the latent space used throughout the paper, and a few other parameters (e.g., antigen functional EC50s). More details on the Github where the code is hosted: https://github.com/frbourassa/antigen_encoding_theory More neural networks can be trained and more cytokine data processing and fitting can be done using the antigen-encoding-pipeline user interface, also hosted on Github.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.501 | 0.459 |
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".