Missing value imputation methods for gene-sample-time microarray data analysis
Bibliographic record
Abstract
With the recent advances in microarray technology, the expression levels of genes with respect to the samples can be monitored synchronically over a series of time points. Such three-dimensional microarray data, termed gene-sample-time microarray data or GST data for short, may contain missing values. Current microarray analysis methods require complete data sets, and thus, either each row, column or tube containing missing values must be removed from the original GST data, or these missing values must be estimated before analysis. Imputation of missing values is, however, more recommended than removal of data in order to increase the effectiveness of analysis algorithms. In this paper, we extend automated imputation methods, devised for two-dimensional microarray data, to GST data. We implemented imputation methods for GST data based on Singular Value Decomposition (3SVDimpute), K-Nearest Neighbor (3KNNimpute), and gene and sample average methods (3Aimpute), and show that methods based on KNN yield the best results with the lowest normalized root mean squared error.
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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 | 0.001 | 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".