Inferring Transcriptional Regulatory Relationships Among Genes in Breast Cancer: An Application of Bayes' Theorem
Bibliographic record
Abstract
The introduction of Deoxyribonucleic acid (DNA) microarray technologies provides a means of measuring the expression of thousands of genes simultaneously. It has generally sought to revolutionalize biological research by significantly elucidating biological processes. Gene networks may be inferred from such microarray data. Bayes' theorem, in this work is applied to the problem of inferring new transcriptional regulatory relationships among gene products in Breast Cancer. A compendium of human breast epithelial cell probe level microarray data from the Gene Expression Omnibus (GEO) repository was subjected to the Robust Multiarray Average (RMA) procedure for normalization and background correction. A subset of the resulting expression matrix consisting of the expression values of only relevant probe-set identifiers (IDs) representing the genes of interest in the data were extracted with a LISP code. This subset was supplied to a Bayesian Network inference learning algorithm to unearth new regulatory relationships from the data. Variations in parameters of the learning algorithms resulted in the prediction of at least 10 new relationships among genes in breast cancer. Among these were the direct regulatory signaling relationship between S-phase kinase associated protein 2 (SKP2) and the Cell division cycle 25A (CDC25A) and that between the cyclin-dependent kinases regulatory subunit 1 (CKS1B / CDC28) and E2F transcription factor 3 (E2F3). The identified causal networks are potentially useful for understanding complex drug actions and dysfunctional signaling in breast cancer.
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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 | 0.014 | 0.038 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".