Global gene expression patterns spanning 3T3-L1 preadipocyte differentiation
Bibliographic record
Abstract
Adipogenesis is of significant relevance from an agricultural perspective. Traits such as subcutaneous fat thickness, marbling and waste fat are of substantial economic importance in animal production. In order to discover more about the genetic basis of this process, a study was undertaken to examine the changes that occur daily in global gene expression as 3T3-L1 cells differentiate from preadipocyte to adipocyte. Duplicate RNA samples were collected daily during the differentiation process and probed with the Affymetrix U74Av2 GeneChip® microarray to allow the time-course analysis of the gene expression profile in these differentiating cells. Self-organizing maps (SOM) clustering was performed to extract patterns of expression over the course of the experiment (day 0 to day 6). The clustering generated nine distinct expression patterns containing between 74 and 420 genes/ESTs. Functional clusters and important chronological changes in the expression of key genes and gene groups were identified. The pattern of expression observed for many genes not only confirmed what has been shown previously for the early stages of differentiation, but also expanded this pattern to cover the whole differentiation process thus giving a very comprehensive overview of patterns and changes in gene expression over the time course of adipocyte differentiation. Key words: Adipocyte differentiation, gene expression, SOM clustering
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.001 | 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".