Discover Community Create You

Loading publication...

Noodle always counts identifier-free feature events for service operation. With your permission, Google Analytics can also estimate audience and engagement. Search text, publication identifiers, cookies, IP addresses and browser fingerprints are never included in Noodle's first-party event counters.

Privacy details

Article

A hybrid mamba-transformer architecture fusing clinical and genetic features for gestational diabetes mellitus prediction.

PloS one - 1 Jan 2026

Huang Ji, Shi Wenbing, Lin Lan, Wei Zhongliang, Li Qianwen

Abstract excerpt

Gestational diabetes mellitus (GDM) is a common disorder of glucose metabolism during pregnancy. Early GDM prediction is crucial for reducing adverse maternal and neonatal outcomes. This paper proposes a hybrid Mamba-Transformer architecture that aggregates clinical and genetic features for GDM prediction. First, a correlation-driven weighted fusion method for clinical and genetic features is introduced. The...

Read the complete abstract on PubMed

Topics

Share this publication in a Topic to start or enrich a Post.

Share to Diabetes, Gestational
Share to Genetic Predisposition to Disease

Methodology Data sources Editorial policy Privacy Delete account & data About Contact Helena Bioinformatics

Share to Prediction Algorithms

Share to Pregnancy

Source

Open in PubMed Open full text

Identifiers

PMID: 42585183
DOI: 10.1371/journal.pone.0355729
PMCID: PMC13465813

Provenance

PubMed metadata and Literature Corpus relationships. Corpus snapshot updated 8 Sept 2026.

Related research

Semantic proximity does not establish scientific evidence.

Research trail→

Click a neighbor to travelStep 1 · 12 closest

A hybrid mamba-transformer architecture fusing clinical and genetic features for gestational diabetes mellitus prediction.PMID 42585183Read ↗

An Explainable Maximum Expected Utility-Optimized Ensemble for Early Prediction of Gestational Diabetes MellitusSemantic proximity Read ↗

Genetic Architecture and Risk Prediction of Gestational Diabetes Mellitus in over 116,144 Chinese PregnanciesSemantic proximity Read ↗

An Explainable Stacking Ensemble with Cost-Sensitive Threshold Optimization for Gestational Diabetes Risk PredictionSemantic proximity Read ↗

Genetic architecture and risk prediction of gestational diabetes mellitus in Chinese pregnanciesSemantic proximity Read ↗

Identify gestational diabetes mellitus by deep learning model from cell-free DNA at the early gestation stageSemantic proximity Read ↗

Fine-tuning of Genome-Wide Polygenic Risk Scores and Prediction of Gestational Diabetes in South Asian Women.Semantic proximity Read ↗

Early prediction of gestational diabetes mellitus with clinical characteristics, cell-free DNA and genetic variants.Semantic proximity Read ↗

A generative pretrained transformer model for decoding individual glucose dynamics from continuous glucose monitoring dataSemantic proximity Read ↗

Machine Learning-Based Risk Stratification for Gestational Diabetes ManagementSemantic proximity Read ↗

Development and validation of a multivariable genotype-informed gestational diabetes prediction algorithm for clinical use in the Mexican population: insights into susceptibility mechanisms.Semantic proximity Read ↗

A Deep Attention-Based Encoder for the Prediction of Type 2 Diabetes Longitudinal Outcomes from Routinely Collected Health Care DataSemantic proximity Read ↗

Early Prediction of Gestational Diabetes Using Integrated Cell-free DNA Features and Omics-derived Genetic ScoresSemantic proximity Read ↗

⌁Select a neighboring publication to make it the new centre.