PSXVII-4 A comparison of weighted gene co-expression networks in high- and low-feed efficiency dairy cattle.
Bibliographic record
Abstract
Feed efficiency is a trait of growing importance in the field of dairy cattle breeding as feed is often the single largest expense in dairy production systems. However, biological underpinnings of this trait have yet to be definitively characterized. The objective of this study was to identify gene networks that exhibited correlated expression patterns in dairy cattle classified as either high- or low-feed efficiency based on residual feed intake (RFI; kg/d). Data used for analyses was obtained from a previous study and accessed through a publicly-available repository (NCBI GEO: GSE92398). Transcriptome data generated using RNA-Sequencing technology was available from two sets of liver biopsies performed on 19 dairy cattle (10 Jersey & 9 Holstein). Animals of both breeds were selected from a research herd based on extreme RFI and allocated to two groups: HIGH feed efficiency (Holstein [n = 5], Jersey [n = 5]) and LOW feed efficiency (Holstein [n = 4], Jersey [n = 5]). Weighted gene co-expression networks (WGCN) were estimated using the WGCNA package in R for high- and low-feed efficiency groups to identify modules of co-expressed genes exclusively present in either of the groups. Twenty-nine and 38 modules were identified as uniquely co-expressed in high- and low-feed efficiency groups, respectively. In the high-feed efficiency group, modules of co-expressed genes were composed of 4336 genes that play crucial roles in biological processes related to feed efficiency, such as regulation of mitochondrial metabolism. In the low-feed efficiency group, 3940 genes responsible for regulation of carbohydrate and protein absorption were found to be in networks differentially co-expressed vs. high efficiency group co-expression networks. Genes involved in uniquely co-expressed networks and the positional markers located within those candidate genes will be further analyzed to elucidate functional relationships of these genes and how they contribute to feed efficiency in dairy cattle.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".