Hierarchical Cluster analysis of SAGE data for cancer profiling
Bibliographic record
Abstract
In this paper we present a method for clustering SAGE (Serial Analysis of Gene Expression) data to detect similarities and dissimilarities between different types of cancer on the subcellular level. The data, however, is extremely high dimensional, and due to the method of measurement, there are many errors as well as missing values in the data, challenging any clustering algorithm. Therefore, we introduce special pre-processing techniques to reduce these errors and to restore missing data. These techniques are tailored to the process that generates the data, making only very conservative changes. Furthermore, we present a new subspace selection technique to identify a relevant subset of attributes (genes) using the Wilcoxon test. This is a general technique that can be applied to select subspaces for the purpose of clustering whenever some high-level categories of interest are known for the data (such as cancerous and noncancerous) . Finally, we discuss the results of the application of the clustering algorithm OPTICS to the SAGE data, before and after our preprocessing steps. 1.
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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".