Identifying subtype specific network-biomarkers of breast cancer survivability
Bibliographic record
Abstract
Current studies of breast cancer find small subsets of gene biomarkers able to accurately predict the survivability of patients. In these studies, the selected genes are not necessarily functionally related, and hence, they may not correctly indicate the molecular mechanism behind breast cancer survivability. Also, several studies have shown there is a very low overlap between the different respective biomarkers subsets for the same cancer disease. To improve the robustness of classification performance and stability of detected biomarkers, recent methods take existing knowledge on relations between genes into account in the classifier, by aggregating functionality related genes to produce discriminative gene subnetworks called network-biomarkers. In this paper, given a breast cancer dataset of patients with different subtypes, we devise a novel network-based approach by integrating protein-protein interaction network (PPI) with gene expression data (1) to identify the network-biomarkers (metagene) of breast cancer survivability and (2) to predict the survivability of breast cancer patients based on subtypes. Our method uses the concept of seed gene for identification of network-biomarkers, ADASYN to solve class imbalance and random forest to predict survivability of patients. We obtained best classification performance with distance 3 from seed gene protein where the gmean, fl-measure and accuracy are respectively 0.900, 0.800 and 90.34%. The maximum size of a network biomarkers with distance 3 is 9. Maximum 34 genes are needed to predict survivability of patients.
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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 | 0.002 |
| 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".