ReviewJournal of translational medicine2026
MicroRNAs in endometriosis: bioinformatics resources, machine learning strategies, and multi-omics perspectives.
Review in Journal of translational medicine, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Authors and funding
8 authors.
Funding
Abstract
backgroundEndometriosis is a heterogeneous gynecological disorder characterized by chronic pain, infertility, and substantial impairment of quality of life. Increasing evidence indicates that microRNAs (miRNAs) are key regulators of endometriosis pathogenesis through their effects on inflammation, angiogenesis, cell proliferation, fibrosis, and hormone-responsive pathways.
methodsIn this review, we summarize the biological roles of miRNAs in endometriosis and discuss their emerging value as diagnostic biomarkers and therapeutic targets. We further examine major bioinformatics resources and analytical tools used in miRNA research, including databases, target prediction platforms, and expression profiling approaches, with emphasis on their relevance and limitations in the context of endometriosis. In addition, we review recent advances in machine learning and deep learning for miRNA identification, target prediction, regulatory network reconstruction, and miRNA-disease association modeling. Particular attention is given to multi-omics integration strategies, which may better capture the molecular heterogeneity of endometriosis and improve biologically informed stratification.
resultsThis review highlights the key roles of miRNAs in endometriosis-related inflammation, angiogenesis, proliferation, fibrosis, and hormone-responsive signaling, and summarizes their potential as non-invasive biomarkers and therapeutic targets. It also emphasizes the value of bioinformatics, machine learning, and multi-omics approaches in identifying clinically relevant miRNA signatures, while acknowledging current challenges in standardization, validation, and interpretability.
conclusionsFuture studies should prioritize standardized multicenter datasets, explainable artificial intelligence, and integrative multi-omics frameworks to develop robust and clinically applicable miRNA-based diagnostic and therapeutic strategies for endometriosis.
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