Bioinformatic Approach to Identify Chaperone Pathway Relationship from Large-Scale Interaction Networks
Bibliographic record
Abstract
We describe a computational protocol to identify functional modules and pathway relationship of chaperones based on physical interaction data derived from high-throughput proteomic experiments. The protocol first identifies interacting proteins shared by the different chaperone systems to organize the chaperones into functional modules. The chaperone functional modules represent groups of chaperones that are involved in mediating the folding of the shared interacting proteins. Either the chaperones in a module can function along a single folding pathway of a given substrate protein or the substrate protein might have two or more different folding pathways that the chaperones act on independently. As described in our computational protocol, probabilities of these pathway relationships between two chaperones in a two-component chaperone module can be determined using whole-genome expression and cellular pathways as reference. This protocol is potentially useful for identifying functional modules and pathway relationships in other biological systems that involve multiple proteins with many identified interactions.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".