FUNCTIONAL CENTRALITY: DETECTING LETHALITY OF PROTEINS IN PROTEIN INTERACTION NETWORKS
Bibliographic record
Abstract
Identifying lethal proteins is important for understanding the intricate mechanism gov-erning life. Researchers have shown that the lethality of a protein can be computed based on its topological position in the protein-protein interaction (PPI) network. Performance of current approaches has been less than satisfactory as the lethality of a protein is a functional characteristic that cannot be determined solely by network topology. Further-more, a significant number of lethal proteins have low connectivity in the interaction networks but are overlooked by most current methods. Our work reveals that a protein’s lethality correlates more strongly with its “func-tional centrality ” than pure topological centrality. We define functional centrality as the topological centrality within a subnetwork of proteins with similar functions. Eval-uation experiments on four Saccharomyces cerevisiae PPI datasets showed that NFC performed significantly better than all the other existing computational techniques. Our method was able to detect low connectivity lethal proteins that were previously unde-tected by conventional methods. The results and an online version of NFC is available
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 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.011 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".