Mathematical Measures of Network Complexity for Cancer Signaling Networks
Bibliographic record
Abstract
The 5-year survival for patients afterdiagnosis/treatment is strongly dependent on tumor type. Prostate cancer patients have a >99% chance of survival past 5-years since diagnosis, while pancreatic patients have <6% chance of survival past 5-years. Since each cancer type has its own molecular signaling network, we asked if there are “signatures” embedded in these networks that inform us as to the 5-year survival. In other words, are there statistical metrics of the network that correlate with survival? And further, if there are, can such signatures provide clues to selecting new therapeutic targets? From the KEGG Cancer Pathway database we computed several conventional and some less conventional network statistics. In particular we found a high correlation (R2 = 0.7) between degree-entropy and 5-year survival based on the SEER database. This correlation suggests that cancers that have a more complex molecular pathway are more refractory than those with less complex molecular pathway. We also found potential new targets by computing the betweenness – a statistical metric of the centrality of a node – for the molecular networks. We have also investigated algebraic and topological indices for network complexity for protein-protein interaction networks of 11 human cancers. We have found evidence that greater network complexity is associated with lower five year survival probabilities. Moreover, we identify several protein families (PIK, ITG, AKT) that are repeated motives in many of the cancer pathways. Our results can aide in identification of promising targets for anti-cancer drugs.
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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 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".