Combining singular value decomposition and t-test into hybrid approach for significant gene extraction from microarray data
Bibliographic record
Abstract
Significant gene extraction from microarray data is a challenging problem which is of great interest to researchers in Computational Biology, Medicine, Computer Science and Statistics. A number of methods have been proposed for extracting the smallest number of genes which can accurately classify different samples. Most of these methods ignore the fact that microarray data is mostly noisy. For instance, only using a statistical t-test has been shown to be insufficient since it result in a high false discovery rate. Recently, a singular value decomposition (SVD) based approach was proposed for time series microarray data reduction, however it turned out not to be efficient for classifying microarray data. To overcome the shortcomings of these approaches, this paper proposes two methods to reduce false discovery rates. The first method involves an iterative t-test which finds the p-value for each gene under perturbation by eliminating one sample at a time. It eliminates weak noisy genes by dropping any gene which does not show significant p-value under all the conditions. The second method is a hybrid process which adapts a combination of the SVD and the t-test. It considers the entropy of all the data, and thus takes the correlation between genes into account. Classification accuracy is used to validate the significance of the extracted genes. The reported test results on two datasets demonstrate the applicability and effectiveness of the two proposed 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 | 0.009 | 0.021 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".