Multi-Objective Approach for Identifying Cancer Subnetwork Markers
Bibliographic record
Abstract
Abstract Identifying genetic markers for cancer is one of the main challenges in the recent researches. Between different cohorts of genetic markers such as genes or a group of genes like pathways or sub-network, identifying functional modules like subnetwork markers has been more challenging. Network-based classification methods have been successfully used for finding effective cancer subnetwork markers. Combination of metabolic networks and molecular profiles of tumor samples has led researchers to a more accurate prediction of subnetwork markers. However, topological features of the network have not been considered in the activity of the subnetwork. Here, we apply a novel protein-protein interaction network-based classification method that considers topological features of the network in addition to the expression profiles of the samples. We have considered the problem of identifying cancer subnetwork markers as a multi-objective problem which in this approach, each subnetwork’s activity level is measured according to both objectives of the problem; Differential expression level of the genes and topological features of the nodes in the network. We found that the subnetwork markers identified by this method achieve higher performance in the classification of cancer outcome in comparison to the other subnetwork markers.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 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".