Obtaining biomarkers in cancer progression from outliers of time-series clusters
Bibliographic record
Abstract
Studying the expression of transcripts throughout the various stages of prostate cancer may provide insight into the factors that influence the progression of the disease. Moreover, it may also reveal outlier transcripts, which have different trends than the majority of the transcripts. In this study, we use a time-series profile hierarchical clustering method to separate dissimilar groups of aligned transcripts that have maximum distance with the other group expression patterns throughout the various stages/sub-stages of prostate cancer progression. The isolated outliers can serve as biomarkers in analyzing different stages/sub-stages. This paper suggests that the combination of proper clustering, distance function and index validation for clusters are suitable model to find a pattern of trending for transcript abundance throughout different prostate cancer stages/sub-stages. The stages/sub-stages represent the time points, and the growth of the transcript abundance throughout those time points are cubic spline interpolated. The trending throughout those stages can lead to understanding the relationships among the transcripts and provide a better analysis of prostate cancer development through stages.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".