Identifying functional modules in the physical interactome of <b><i>Saccharomyces cerevisiae</i></b>
Bibliographic record
Abstract
Reliable information on the physical and functional interactions between the gene products is an important prerequisite for deriving meaningful system-level descriptions of cellular processes. The available information about protein interactions in Saccharomyces cerevisiae has been vastly increased recently by two comprehensive tandem affinity purification/mass spectrometry (TAP/MS) studies. However, using somewhat different approaches, these studies produced diverging descriptions of the yeast interactome, clearly illustrating the fact that converting the purification data into accurate sets of protein-protein interactions and complexes remains a major challenge. Here, we review the major analytical steps involved in this process, with special focus on the task of deriving complexes from the network of binary interactions. Applying the Markov Cluster procedure to an alternative yeast interaction network, recently derived by combining the data from the two latest TAP/MS studies, we produce a new description of yeast protein complexes. Several objective criteria suggest that this new description is more accurate and meaningful than those previously published. The same criteria are also used to gauge the influence that different methods for deriving binary interactions and complexes may have on the results. Lastly, it is shown that employing identical procedures to process the latest purification datasets significantly improves the convergence between the resulting interactome descriptions.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".