Two-way learning with one-way supervision for gene expression data
Bibliographic record
Abstract
BACKGROUND: A family of parsimonious Gaussian mixture models for the biclustering of gene expression data is introduced. Biclustering is accommodated by adopting a mixture of factor analyzers model with a binary, row-stochastic factor loadings matrix. This particular form of factor loadings matrix results in a block-diagonal covariance matrix, which is a useful property in gene expression analyses, specifically in biomarker discovery scenarios where blood can potentially act as a surrogate tissue for other less accessible tissues. Prior knowledge of the factor loadings matrix is useful in this application and is reflected in the one-way supervised nature of the algorithm. Additionally, the factor loadings matrix can be assumed to be constant across all components because of the relationship desired between the various types of tissue samples. Parameter estimates are obtained through a variant of the expectation-maximization algorithm and the best-fitting model is selected using the Bayesian information criterion. The family of models is demonstrated using simulated data and two real microarray data sets. The first real data set is from a rat study that investigated the influence of diabetes on gene expression in different tissues. The second real data set is from a human transcriptomics study that focused on blood and immune tissues. The microarray data sets illustrate the biclustering family's performance in biomarker discovery involving peripheral blood as surrogate biopsy material. RESULTS: The simulation studies indicate that the algorithm identifies the correct biclusters, most optimally when the number of observation clusters is known. Moreover, the biclustering algorithm identified biclusters comprised of biologically meaningful data related to insulin resistance and immune function in the rat and human real data sets, respectively. CONCLUSIONS: Initial results using real data show that this biclustering technique provides a novel approach for biomarker discovery by enabling blood to be used as a surrogate for hard-to-obtain tissues.
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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 | 0.022 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".