ArticleAging2023
Identification and validation of diagnostic signature genes in non-obstructive azoospermia by machine learning.
Article in Aging, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 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
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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Who cites it
5 citing papers in PubMed, 7 citations in OpenAlex.
- Identification of biomarkers related to amino acid metabolism in nonobstructive azoospermia by bioinformatics.Medicine · 2026Article
- GutFrontiers in cellular and infection microbiology · 2026Article
- Chronic oral administration of L-carnitine induces testicular injury: in vivo evidence.International urology and nephrology · 2025Article
- Single-cell sequencing in non-obstructive azoospermia: insights from primary and re-analysis studies.Frontiers in endocrinology · 2025Review
- The Role of Tubulin Polymerization-Promoting Protein2 (TPPP2) in Spermatogenesis: A Narrative Review.International journal of molecular sciences · 2024Review
Corrections and comments
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Authors and funding
6 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Non-obstructive azoospermia (NOA) is a common cause of male infertility, and no specific diagnostic indicators exist. In this study, we used human testis datasets GSE45885, GSE45887, and GSE108886 from GEO database as training datasets, and screened 6 signature genes (all lowly expressed in the NOA group) using Boruta algorithm and Lasso regression: C12orf54, TSSK6, OR2H1, FER1L5, C9orf153, XKR3. The diagnostic efficacy of the above genes was examined by constructing models with LightGBM algorithm: the AUC (Area Under Curve) of both ROC and Precision-Recall curves for internal validation was 1.0 (
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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.