Using Gene Clustering to Identify Discriminatory Genes with Higher Classification Accuracy
Bibliographic record
Abstract
A single DNA microarray measures thousands to tens of thousands of gene expression levels, but experimental datasets normally consist of much fewer such arrays, typically in tens to hundreds, taken over a selection of tissue samples. The biological interpretation of these data relies on identifying subsets of induced or repressed genes that can be used to discriminate various categories of tissue, to provide experimental evidence for connections between a subset of genes and the tissue pathology. A variety of methods can be used to identify discriminatory gene subsets, which can be ranked by classification accuracy. But the high dimensionality of the gene expression space, coupled with relatively fewer tissue samples, creates the dimensionality problem: gene subsets that are too large to provide convincing evidence for any plausible causal connection between that gene subset and the tissue pathology. We propose a new gene selection method, clustered gene selection (CGS) which, when coupled with existing methods, can identify gene subsets that overcome the dimensionality problem and improve classification accuracy. Experiments on eight real datasets showed that CGS can identify many more cancer related genes and clearly improve classification accuracy, compared with three other non-CGS based gene selection methods
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".