Data on mammary gland microRNAs expression, their predicted gene targets and corresponding pathway analysis in female mice receiving flaxseed or its oil and secoisolariciresinol diglucoside components
Bibliographic record
Abstract
Dietary flaxseed may act via microRNAs (miRNAs) to affect the health of the mammary gland. These data are in support of the article entitled "Effects of flaxseed and its components on mammary gland miRNome: identification of potential biomarkers to prevent breast cancer development" [1]. Here, we provide miRNA expression data obtained from NanoString nCounter® profiling of mammary gland RNA from C57BL/6 female mice who received a control diet or isocaloric diets containing 10% FS, 3.67% FSO, or 0.15% SDG for 21 days. The raw miRNA data were deposited at the NCBI Gene Expression Omnibus (GEO) database (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE193847) under the accession number GSE193847. We also identified diet-associated miRNA-gene targets and corresponding enriched pathways. These data can be found at the HARVARD Dataverse (https://doi.org/10.7910/DVN/3ZNYES). These data will be valuable as a reference to understand the effects of FS versus its components and to study responses to these ingredients in hosts of different genetic backgrounds, sex and age. These data will contribute to future investigations regarding mechanisms underlying FS effects within the mammary gland.
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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) | 0.001 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".