CellFrame: A Data Structure for Abstraction of Cell Biology Experiments and Construction of Perturbation Networks
Bibliographic record
Abstract
Different cell types could respond to the same set of stimuli in very different ways, so it is important to collect and integrate these experimental data in a cell-type specific manner in order to properly model these processes. In practice, however, cellular or biochemical models were usually constructed separately, with data from multiple cell sources, making it difficult to compare or combine these models. To circumvent this problem, we propose to conduct the model integration at the data level. To facilitate this purpose we have designed a data structure (CellFrame) to store cell-specific data, such as cellular component measurement and stimulus-response, and to infer cell perturbation network (a cellular network representing the stimulus-response relationships between the cellular and/or environmental components). The CellFrame database consists of two data types: data classes and supporting classes. Data classes constitute three modules: cell component measurement, qualitative cell perturbation, and quantitative cell perturbation. Supporting classes serve as adaptors, linking to external molecular or literature databases. We have implemented an initial version of CellFrame, which contains data collected from reported experiments on human brain astrocytoma and colorectal cancer cell lines (http://cellframe.bioknowledge.org). Perturbation networks are inferred following Boolean differential calculus. CellFrame will provide an opportunity to integrate cell biological data from a wide variety of experimental groups to help build cell models and design further experiments.
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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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.005 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.025 | 0.011 |
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".