Outlier Genes as Biomarkers of Breast Cancer Survivability in Time-Series Data
Bibliographic record
Abstract
Studying gene expression through various time intervals of breast cancer survival may provide new insights into the recovery from the disease. In this work, we propose a hierarchical clustering method to separate dissimilar groups of gene time-series profiles, which have the furthest distances from the rest of the profiles throughout different time intervals. The isolated outliers can be used as potential biomarkers of Breast Cancer survivability. Gene expressions throughout those time points are cubic spline interpolated to create a trending profile for each gene. After universally aligning the profiles to minimize the vertical area between each pair of profiles, we cluster the genes using hierarchical clustering based on minimized vertical distances [1]. An appropriate number of clusters was chosen based on the profile alignment and agglomerative clustering (PAAC) index as well as visual observations of the clusters. Our study suggests that the combination of proper clustering, distance function and index validation for clusters is a suitable model to identify genes as informative biomarkers of breast cancer survivability.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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".