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<p>A drift-aware, generative, and intervention-guided paradigm for early risk assessment of childhood type 2 diabetes using longitudinal multi-omics integration is provided in this study. The suggested method simulates developmental methylation patterns using transcriptome and proteome measurements. The hierarchical representation preserves intra-layer structure and inter-layer interactions while combining molecular layers. For temporal drift, adaptive penalties that favor age-aligned biological progression improve coherence across measurements and reduce divergence that commonly arises when multi-omics signals are modeled independently. Developmentally realistic forward trajectories are generated by this architecture. This design finds directionally consistent CpG loci-regulatory driver linkages using causal graph reasoning. Sparse pruning prioritizes metabolic pathway interactions over unlikely edges. To retain biological fidelity and anchor drift magnitude against clinical advancement, diffusion-based denoising refines acquired methylation sequences in the generating stage. Counterfactual simulation layers examine risk trajectories with lifestyle changes including persistent activity and anthropometric improvements. Individualised simulations reveal how preventive strategies improve glycemic outcomes. Cross-modal coherence, temporal consistency, and trajectory stability are better than recent generative benchmarks without drift constraints or intervention-aware simulation using publically accessible pediatric longitudinal datasets. The integrative technique reduces longitudinal projection cross-modal divergence and produces coherent temporal manifolds for early screening and tiered prevention. Privacy-sensitive contexts benefit from downstream augmentation for data-constrained clinical investigations with synthetic, drift-aligned cohorts. Besides risk estimation, the framework may examine molecular reconfiguration during early metabolic dysregulation. Characterizing directional influence shifts over time gives the model mechanistic insights into regulatory perturbations. These computational tools improve early diabetes risk assessment, precision prevention, and translational research in pediatric metabolic health sets by integrating multi-omics, generative modeling, and intervention inference sets.</p>

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Design of an integrated drift-aware generative and predictive modeling for childhood epigenetics: multi-modal, causal, and intervention-aware frameworks for diabetes risk simulations

  • 期刊论文

  • 发表日期：

    2025年12月19日

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  • Rasika S. Badre,
  • Ajay Kumar

摘要

A drift-aware, generative, and intervention-guided paradigm for early risk assessment of childhood type 2 diabetes using longitudinal multi-omics integration is provided in this study. The suggested method simulates developmental methylation patterns using transcriptome and proteome measurements. The hierarchical representation preserves intra-layer structure and inter-layer interactions while combining molecular layers. For temporal drift, adaptive penalties that favor age-aligned biological progression improve coherence across measurements and reduce divergence that commonly arises when multi-omics signals are modeled independently. Developmentally realistic forward trajectories are generated by this architecture. This design finds directionally consistent CpG loci-regulatory driver linkages using causal graph reasoning. Sparse pruning prioritizes metabolic pathway interactions over unlikely edges. To retain biological fidelity and anchor drift magnitude against clinical advancement, diffusion-based denoising refines acquired methylation sequences in the generating stage. Counterfactual simulation layers examine risk trajectories with lifestyle changes including persistent activity and anthropometric improvements. Individualised simulations reveal how preventive strategies improve glycemic outcomes. Cross-modal coherence, temporal consistency, and trajectory stability are better than recent generative benchmarks without drift constraints or intervention-aware simulation using publically accessible pediatric longitudinal datasets. The integrative technique reduces longitudinal projection cross-modal divergence and produces coherent temporal manifolds for early screening and tiered prevention. Privacy-sensitive contexts benefit from downstream augmentation for data-constrained clinical investigations with synthetic, drift-aligned cohorts. Besides risk estimation, the framework may examine molecular reconfiguration during early metabolic dysregulation. Characterizing directional influence shifts over time gives the model mechanistic insights into regulatory perturbations. These computational tools improve early diabetes risk assessment, precision prevention, and translational research in pediatric metabolic health sets by integrating multi-omics, generative modeling, and intervention inference sets.