Alternative Pathway Approach for Automating Analysis and Validation of Cell Perturbation Networks and Design of Perturbation Experiments
Bibliographic record
Abstract
Cell perturbation data are a very important resource to analyze and reconstruct cell-signaling networks. To facilitate the utilization of this type of data and enable large-scale and automated network reconstruction effort, we have already developed a data structure for storing cell perturbation results and deducing perturbation networks (CellFrame). For automating network analysis, we here propose a computational method called the "alternative pathway approach" (ALPA) in this work. This method can validate the signaling networks with conditional perturbation data extracted from published experiments, and can suggest additional tests to improve the network. It searches the alternative pathway space between all pairs of nodes, constructs pathnets (set of pathways between two nodes) and validates the network edges using conditional perturbation. For pathnets without conditional data, experiments with the fewest number of perturbations or the most parsimonious are designed. For pathnets without experimentally derived or detected pair-wise interactions, ALPA can predict the potential effects or propose additional pathways to expand the existing network. We have tested the ALPA method on the TNFalpha-MAPK signaling cascade; the reconstructed network is consistent with the consensus model. We also used the ALPA algorithm to analyze a yeast gene perturbation network, using the data from the Rosetta compendium of expression profiles, which demonstrates that it can be also used for large-scale analysis. We compared the performance of the ALPA approach with Boolean and Bayesian network algorithms for their efficiency and accuracy, respectively, in network construction.
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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 | 0.005 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".