Identification of Biomarkers for Predicting the Overall Survival of Ovarian Cancer Patients: a Sparse Group Lasso Approach
Bibliographic record
Abstract
Next-generation sequencing has been routinely applied to cancer biology, making it possible for researchers to elucidate the molecular mechanisms underlying cancer initiation and progression. However, how to identify oncomarkers from massive complex genomic data poses a great challenge for both modeling and computing. In this paper, we propose a novel computational pipeline to identify genes related to the overall survival of ovarian cancer patients from the rich Cancer Genome Atlas data. Different from the existing studies, we incorporate dependence structure among genes and pathway information into the variable selection. Firstly, the dimensionality of the ovarian cancer data is reduced by a novel stepwise feature screening which mimics the hierarchy of the underlying causal network. The second step of the pipeline is to divide genes into clusters with distinct cellular functions by k-means, x-means and PAMSAM learning algorithms. In the final step, we fit a cox proportional hazard model with a sparse group lasso penalty for further variable selection. Of the 115 genes in the final list, many were reported to be associated with cancer initiation or progression in the literature. In addition, we find several gene families including the NEK family and RNF family, which are closely associated with the survival of ovarian cancer 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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| 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".