A FLEXIBLE NONPARAMETRIC APPROACH TO FIND CANDIDATE GENES ASSOCIATED WITH DISEASE IN MICROARRAY EXPERIMENTS
Bibliographic record
Abstract
Very often biologists are interested to know the biological function of a particular gene. Its true biological function may depend on other genes. Finding other genes in the same biological pathway of that gene may enhance further understanding of its biological function. Therefore, we are interested in finding other candidate genes whose expression values are highly correlated with that of a "seed" gene. The "seed" gene, which is known and associated with a disease, is used as a reference to extract candidate genes from microarray experiments and enriched pathways. We propose a nonparametric procedure for selecting the candidate genes. The expression levels for these candidate genes are correlated with that of a "seed" gene in microarray experiments. The proposed test statistic compares two Area Under Receiver Operating Characteristic Curves (AUC) for gene pairs, taking implicit correlation between two AUCs into account. The performance of our method is compared to the other well-known methods through the use of simulation and real data analysis.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 teacher head, 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".