Application of model selection technique in chemogenomic data analysis
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract With the advent of high‐throughput chemogenomic data, it becomes crucially important to extract the important genes which influence the drug activities among the huge number of candidate genes. By employing model selection technique, especially designed for high‐dimensional data, we propose to develop a systematic approach to construct the network elucidating the dependency relationships among the drugs and the genes. Based on the extended Bayesian Information Criterion, we are able to select the best parsimonious network structure. A real National Cancer Institute (NCI)‐60 panel data set is analyzed to demonstrate the utility of the method. The biological implications of the results are discussed. Copyright © 2009 Wiley Periodicals, Inc. Statistical Analysis and Data Mining 2: 186–191, 2009
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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 | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.003 | 0.001 |
| 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 it