Omics Multi-Layers Networks Provide Novel Mechanistic and Functional Insights Into Fat Storage and Lipid Metabolism in Poultry
Bibliographic record
Abstract
Fatty acid metabolism in poultry has a major impact on production and disease resistance traits. According to the high rate of interactions between lipid metabolism and its regulating properties, a holistic approach is necessary. To study omics multilayers of adipose tissue and identification of genes and miRNAs involved in fat metabolism, storage and endocrine signaling pathways in two groups of broiler chickens with high and low abdominal fat, as well as high-throughput techniques, were used. The gene-miRNA interacting bipartite and metabolic-signaling networks were reconstructed using their interactions. In the analysis of microarray and RNA-Seq data, 1,835 genes were detected by comparing the identified genes with significant expression differences (p.adjust < 0.01, fold change ≥ 2 and ≤ -2). Then, by comparing between different data sets, 34 genes and 19 miRNAs were detected as common and main nodes. A literature mining approach was used, and seven genes were identified and added to the common gene set. Module finding revealed three important and functional modules, which were involved in the peroxisome proliferator-activated receptor (PPAR) signaling pathway, biosynthesis of unsaturated fatty acids, Alzheimer's disease metabolic pathway, adipocytokine, insulin, PI3K-Akt, mTOR, and AMPK signaling pathway. This approach revealed a new insight to better understand the biological processes associated with adipose tissue.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".