ArticleAnimals : an open access journal from MDPI2022
Machine Learning-Based Co-Expression Network Analysis Unravels Potential Fertility-Related Genes in Beef Cows.
Article in Animals : an open access journal from MDPI, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
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Who cites it
8 citing papers in PubMed.
- Multi-Omics Analysis of the Effects ofInternational journal of molecular sciences · 2026Article
- Network-based multi-omics approaches to identify molecular signatures associated with pregnancy status in beef heifers.Frontiers in genetics · 2026Article
- Article
- Key mRNAs and lncRNAs of pituitary that affect the reproduction of FecB + + small tail han sheep.BMC genomics · 2024Article
- Mapping Expression Quantitative Trait Loci Targeting Candidate Genes for Pregnancy in Beef Cows.Biomolecules · 2024Article
- Harnessing Genomics and Transcriptomics Approaches to Improve Female Fertility in Beef Cattle-A Review.Animals : an open access journal from MDPI · 2023Review
- mRNA Signatures in Peripheral White Blood Cells Predict Reproductive Potential in Beef Heifers at Weaning.Genes · 2023Article
- miRNA expression profiles of peripheral white blood cells from beef heifers with varying reproductive potential.Frontiers in genetics · 2023Article
Corrections and comments
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Authors and funding
4 authors.
Funding
Abstract
Reproductive failure is still a challenge for beef producers and a significant cause of economic loss. The increased availability of transcriptomic data has shed light on the mechanisms modulating pregnancy success. Furthermore, new analytical tools, such as machine learning (ML), provide opportunities for data mining and uncovering new biological events that explain or predict reproductive outcomes. Herein, we identified potential biomarkers underlying pregnancy status and fertility-related networks by integrating gene expression profiles through ML and gene network modeling. We used public transcriptomic data from uterine luminal epithelial cells of cows retrospectively classified as pregnant (P, n = 25) and non-pregnant (NP, n = 18). First, we used a feature selection function from BioDiscML and identified
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.