ArticleScientific reports2020
Machine learning approach to integrated endometrial transcriptomic datasets reveals biomarkers predicting uterine receptivity in cattle at seven days after estrous.
Article in Scientific reports, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
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Who cites it
13 citing papers in PubMed, 27 citations in OpenAlex.
- Application of omics technologies to identify reproductive phenotypes in the context of assisted reproductive technologies for cattle.Animal reproduction · 2026Article
- Gene profiles of peripheral white blood cells as potential predictors of pregnancy in embryo-recipient heifers.PloS one · 2025Article
- Involvement of chemokine CXCL12 and its receptor CXCR4 in uterine receptivity and potential relationship to fertility in cattle: a mini review.Frontiers in veterinary science · 2025Review
- Review
- Article
- Solutions to the fertility equation in beef embryo recipients.Animal reproduction · 2024Review
- miRNA expression profiles of peripheral white blood cells from beef heifers with varying reproductive potential.Frontiers in genetics · 2023Article
- BioDiscViz: A visualization support and consensus signature selector for BioDiscML results.PloS one · 2023Article
- Machine Learning-Based Co-Expression Network Analysis Unravels Potential Fertility-Related Genes in Beef Cows.Animals : an open access journal from MDPI · 2022Article
- Identification of differentially expressed long noncoding RNAs in the ovarian tissue of Shal and Sangsari ewes using RNA-seq.Veterinary medicine and science · 2022Article
- Progesterone-dependent and progesterone-independent modulation of luminal epithelial transcription to support pregnancy in cattle.Physiological genomics · 2022Article
- Genes and regulatory mechanisms associated with experimentally-induced bovine respiratory disease identified using supervised machine learning methodology.Scientific reports · 2021Article
- Transcriptomic Analysis of STAT1/3 in the Goat Endometrium During Embryo Implantation.Frontiers in veterinary science · 2021Article
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Authors and funding
2 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The main goal was to apply machine learning (ML) methods on integrated multi-transcriptomic data, to identify endometrial genes capable of predicting uterine receptivity according to their expression patterns in the cow. Public data from five studies were re-analyzed. In all of them, endometrial samples were obtained at day 6-7 of the estrous cycle, from cows or heifers of four different European breeds, classified as pregnant (n = 26) or not (n = 26). First, gene selection was performed through supervised and unsupervised ML algorithms. Then, the predictive ability of potential key genes was evaluated through support vector machine as classifier, using the expression levels of the samples from all the breeds but one, to train the model, and the samples from that one breed, to test it. Finally, the biological meaning of the key genes was explored. Fifty genes were identified, and they could predict uterine receptivity with an overall 96.1% accuracy, despite the animal's breed and category. Genes with higher expression in the pregnant cows were related to circadian rhythm, Wnt receptor signaling pathway, and embryonic development. This novel and robust combination of computational tools allowed the identification of a group of biologically relevant endometrial genes that could support pregnancy in the cattle.
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