Identification of biologically significant genes using Gene Ontology (GO) and pathways analysis (144.16)
Bibliographic record
Abstract
Abstract Due to the small sample size and high dimensionality, it is not always possible to identify all of the biologically relevant genes from microarray analysis by simply selecting an arbitrary fold change and p-value thresholds. Such concerns are especially relevant in the analysis of immune response. It has been long known that large variability exist in immune responsiveness with the coefficient of variation (CV) as high as 230% reported for antibody response. Similarly, immune related genes were shown to have CV in excess of 100%. As a consequence, some genes involved in immune response will never be detected as differentially expressed as the number of replications required to detect statistically significant effects may be unobtainable even in well-designed experiments. To better understand which biological pathways were affected by allergenic response to ovomucoid treatment in mouse spleen, two gene sets for GO analysis were selected using different selection criteria. First set included differentially expressed genes that were biologically and statistically significant between treatments (>1.5 fold, P<0.05). Genes for the second gene set were selected based only on their biological significance (>1.5 fold). It is demonstrated that not only new biologically significant genes were identified in previously detected GO and pathway categories (p<0.05) but completely new categories (p<0.05) were detected using second gene set.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| 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".