ArticleBriefings in bioinformatics2023
Identify gestational diabetes mellitus by deep learning model from cell-free DNA at the early gestation stage.
Article in Briefings in bioinformatics, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.
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Who cites it
15 citing papers in PubMed, 18 citations in OpenAlex.
- Artificial Intelligence-Driven Antimicrobial Peptide Discovery: Prediction, Generation, Mining and Optimization.Probiotics and antimicrobial proteins · 2026Review
- Early prediction of gestational diabetes mellitus with clinical characteristics, cell-free DNA and genetic variants.Journal of translational medicine · 2026Article
- Predictive performance of artificial intelligence algorithms for gestational diabetes mellitus in pregnant women: a protocol for systematic review and meta-analysis.Systematic reviews · 2026Article
- BPX-Net: biomarker-preserved explainable networks for disease diagnosis and prognosis.BioData mining · 2026Article
- Gut microbiota dysbiosis, circulating microbial genetic traces, and their role in gestational diabetes.World journal of diabetes · 2026Review
- The Placenta in Gestational Diabetes: An Integrated Review on Metabolic Pathways, Genetic, Epigenetic and Ultrasound Biomarkers for Clinical Perspectives.International journal of molecular sciences · 2026Review
- Artificial intelligence for early prediction of gestational diabetes mellitus and preeclampsia: a systematic review of machine learning models and clinical decision support systems.Frontiers in artificial intelligence · 2026Review
- Next-generation antifungal peptide discovery: the synergy of artificial intelligence and omics technologies.World journal of microbiology & biotechnology · 2025Review
- Optimising hyperparameters with a tree structured Parzen estimator to improve diabetes prediction.Scientific reports · 2025Article
- Artificial Intelligence in Gestational Diabetes Care: A Systematic Review.Journal of diabetes science and technology · 2025Review
- Genetic architecture and risk prediction of gestational diabetes mellitus in Chinese pregnancies.Nature communications · 2025Article
- Validating Multicenter Cohort Circular RNA Model for Early Screening and Diagnosis of Gestational Diabetes Mellitus.Diabetes & metabolism journal · 2025Article
- Factors influencing dietary compliance among patients with gestational diabetes mellitus: a retrospective analysis.American journal of translational research · 2025Article
- Advances in cfDNA research for pregnancy-related diseases.Frontiers in cell and developmental biology · 2025Review
- Longitudinal integrative cell-free DNA analysis in gestational diabetes mellitus.Cell reports. Medicine · 2024Article
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Authors and funding
18 authors at 3 institutions in 1 country.
Funding
Abstract
Gestational diabetes mellitus (GDM) is a common complication of pregnancy, which has significant adverse effects on both the mother and fetus. The incidence of GDM is increasing globally, and early diagnosis is critical for timely treatment and reducing the risk of poor pregnancy outcomes. GDM is usually diagnosed and detected after 24 weeks of gestation, while complications due to GDM can occur much earlier. Copy number variations (CNVs) can be a possible biomarker for GDM diagnosis and screening in the early gestation stage. In this study, we proposed a machine-learning method to screen GDM in the early stage of gestation using cell-free DNA (cfDNA) sequencing data from maternal plasma. Five thousand and eighty-five patients from north regions of Mainland China, including 1942 GDM, were recruited. A non-overlapping sliding window method was applied for CNV coverage screening on low-coverage (~0.2×) sequencing data. The CNV coverage was fed to a convolutional neural network with attention architecture for the binary classification. The model achieved a classification accuracy of 88.14%, precision of 84.07%, recall of 93.04%, F1-score of 88.33% and AUC of 96.49%. The model identified 2190 genes associated with GDM, including DEFA1, DEFA3 and DEFB1. The enriched gene ontology (GO) terms and KEGG pathways showed that many identified genes are associated with diabetes-related pathways. Our study demonstrates the feasibility of using cfDNA sequencing data and machine-learning methods for early diagnosis of GDM, which may aid in early intervention and prevention of adverse pregnancy outcomes.
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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.