WEIGHTED GENE CO-EXPRESSION NETWORK ANALYSIS IDENTIFIES BIOLOGICAL PATHWAYS AND BIOMARKER GENES ASSOCIATED WITH CHICKENS' ADAPTATION TO BOTH LOW AND HIGH ALTITUDES
Bibliographic record
Abstract
The main aim of the study was to identify modules, hub genes, and possible pathways linked with hypoxia adaptation in six types of tissues and organs (heart, kidney, liver, lung, muscle, and spleen) at altitudes ranging from 2,300 to 3,500 metersOn a transcription dataset from hypoxia-sensitive tissues, we performed weighted gene co-expression network analysis on 13,940 selected genes, and 10 transcriptional modules in total were detected (Turquoise 196 genes, Purple 27 genes, Blue 196, Brown 182, Yellow 108, Green 79 genes, Red 69 genes, Black 50 genes, Pink 44 genes, and Magenta 37 genes). Furthermore, we discovered that the majority of variable genes were screened by sub-setting 1000 genes; samples belonging to the same tissue clearly clustered together, and the expression in the liver and lung was more associated than in the heart and spleen. Functional enrichment analysis of all genes in 12 selected modules revealed that 9 KEGG pathways were considerably enriched, 13 Gene ontology terms were significantly enriched in the biological process and cellular component pathways, and 15 gene ontology terms were significantly enriched in the molecular function pathway. Through weighted gene co-expression network analysis, the results of this study expand our knowledge of the molecular pathway of catalytic and metabolic activity as a biomarker pathway
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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".