Reproductive endocrinology is entering a precision-medicine era, yet infertility and reproductive disorders remain highly heterogeneous conditions that conventional hormonal assessment alone cannot fully explain. Multi-omics technologies, spanning genomics, epigenomics, and transcriptomics, are revealing molecular signatures of oocyte, sperm, and embryo quality that were previously invisible to standard diagnostic workups. In parallel, oxidative stress and mitochondrial dysfunction are increasingly recognized as central, measurable drivers of gamete and embryo competence. Artificial intelligence and machine learning now offer a practical way to integrate these multi-layered datasets into diagnostic and prognostic tools clinicians can actually use. Despite this progress, translation from omics discovery to validated, clinically actionable biomarkers remains limited, and the field lacks a dedicated forum connecting molecular mechanism to AI-assisted, personalized diagnostics.
This Research Topic aims to advance precision reproductive endocrinology by bringing together molecular biologists, reproductive clinicians, bioinformaticians, and data scientists working on the path from multi-omics discovery to clinically usable diagnostics. We seek to consolidate current evidence on genomic, epigenomic, and transcriptomic biomarkers of infertility and gamete/embryo quality; deepen mechanistic understanding of oxidative stress and mitochondrial dysfunction as drivers of reduced reproductive competence; and showcase AI/machine-learning approaches that integrate multi-omics data to predict infertility risk, gamete and embryo quality, and treatment response. By anchoring computational innovation to validated molecular mechanism, this collection aims to accelerate the translation of precision-medicine tools into everyday reproductive endocrinology practice, ultimately supporting more accurate diagnosis and more personalized treatment decisions for patients.
We welcome original research, systematic and narrative reviews, and perspectives addressing:
o Genomic, epigenomic, and transcriptomic biomarkers of infertility and gamete/embryo quality
o Molecular mechanisms of oxidative stress and mitochondrial dysfunction in oocyte, sperm, and embryo development
o AI- and machine-learning-based integration of multi-omics data for personalized infertility diagnosis, prognosis, and treatment prediction
Contributions addressing both female and male reproductive health, and spanning basic, translational, and clinical perspectives, are equally welcome. We particularly encourage studies that validate candidate biomarkers against clinical outcomes (e.g., IVF success, embryo quality grading) rather than purely exploratory omics profiling, to keep the collection focused on translational, decision-relevant evidence.
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Article types
This Research Topic accepts the following article types, unless otherwise specified in the Research Topic description:
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