OmniBioTwin: A System-of-Twinned-Systems Framework for Health Digital Twins

• URL: http://arxiv.org/abs/2606.11264v1
• Date: Tue, 09 Jun 2026 03:54:49 GMT
• Title: OmniBioTwin: A System-of-Twinned-Systems Framework for Health Digital Twins
• Abstract summary: Health digital twins (HDTs) promise patient-specific modeling and decision support.<n>We propose OmniBioTwin, a framework that organizes HDTs as modular computational entities.<n>We demonstrate OmniBioTwin by instantiating a multiscale twin for glucagon-like peptide-1 signaling pathways in Alzheimer's disease.
• Score: 14.774391664642472
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Health digital twins (HDTs) promise patient-specific modeling and decision support but current approaches remain structurally fragmented: monolithic models that address a single organ or task lack cross-scale fidelity, while system-level twins lack generalizable architectural frameworks. We propose OmniBioTwin, a System-of-Twinned-Systems (SoTS) framework that organizes HDTs as modular computational entities coupled through explicit interaction operators within a multi-layer network architecture. The framework comprises seven coordinated layers - spanning data integration, autonomous twin modeling, cross-scale coupling, temporal synchronization, and human-in-the-loop decision support. We demonstrate OmniBioTwin by instantiating a multiscale twin for glucagon-like peptide-1 (GLP-1) signaling pathways in Alzheimer's disease, illustrating how molecular, cellular, and organ-level twins can be composed and coupled within a unified system.

Related papers

• Toward Federated Cognitive Digital Twins over the Edge-to-Cloud Continuum [47.9194074308038]
  This paper proposes a Federated Cognitive Digital Twin (FCDT) architecture that combines federation and cognition within a unified approach.<n>The architecture distributes intelligence across the edge-to-cloud continuum through local twins, and global twins, which perform system-level reasoning, simulation, and coordination.
  arXiv  Detail & Related papers  (2026-07-23T14:20:59Z)
• Integrating Heterogeneous Digital Twins in Federated Ecosystems [0.8582085540322195]
  textitFederated Digital Twin Ecosystems have emerged as a promising direction.<n>This paper proposes the textitFederation Node Manager, a modular integration mechanism that connects local DTs to a federated environment.<n>We present a conceptual design and a prototype implementation, and demonstrate their feasibility in the smart mobility domain for emergency response scenarios.
  arXiv  Detail & Related papers  (2026-06-22T03:04:11Z)
• NeuroAlign: Hierarchical Multimodal Fusion of Dynamic and Structural Neuroimaging for MCI Analysis [31.200243008831578]
  We propose textitNeuroAlign, a hierarchical framework for structured multimodal fusion.<n>NeuroAlign achieves competitive MCI/SCD detection and preliminary cross-dataset transferability.
  arXiv  Detail & Related papers  (2026-05-31T14:01:15Z)
• STEM Agent: A Self-Adapting, Tool-Enabled, Extensible Architecture for Multi-Protocol AI Agent Systems [0.0]
  We introduce STEM Agent, a modular architecture inspired by biological pluripotency.<n>The framework unifies five interoperability protocols behind a single gateway.<n>A comprehensive 413-test suite validates protocol handler behavior and component integration across all five architectural layers.
  arXiv  Detail & Related papers  (2026-03-22T20:50:53Z)
• scMRDR: A scalable and flexible framework for unpaired single-cell multi-omics data integration [53.683726781791385]
  We introduce a scalable and flexible generative framework called single-cell Multi-omics Regularized Disentangled Representations (scMRDR) for unpaired multi-omics integration.<n>Our method achieves excellent performance on benchmark datasets in terms of batch correction, modality alignment, and biological signal preservation.
  arXiv  Detail & Related papers  (2025-10-28T21:28:39Z)
• GaitMA: Pose-guided Multi-modal Feature Fusion for Gait Recognition [26.721242606715354]
  Gait recognition is a biometric technology that recognizes the identity of humans through their walking patterns. We propose a novel gait recognition framework, dubbed Gait Multi-model Aggregation Network (GaitMA) First, skeletons are represented by joint/limb-based heatmaps, and features from silhouettes and skeletons are respectively extracted using two CNN-based feature extractors.
  arXiv  Detail & Related papers  (2024-07-20T09:05:17Z)
• Neuro-symbolic Explainable Artificial Intelligence Twin for Zero-touch IoE in Wireless Network [61.90504487270785]
  Explainable artificial intelligence (XAI) twin systems will be a fundamental enabler of zero-touch network and service management (ZSM) A reliable XAI twin system for ZSM requires two composites: an extreme analytical ability for discretizing the physical behavior of the Internet of Everything (IoE) and rigorous methods for characterizing the reasoning of such behavior. A novel neuro-symbolic explainable artificial intelligence twin framework is proposed to enable trustworthy ZSM for a wireless IoE.
  arXiv  Detail & Related papers  (2022-10-13T01:08:06Z)
• DG-STGCN: Dynamic Spatial-Temporal Modeling for Skeleton-based Action Recognition [77.87404524458809]
  We propose a new framework for skeleton-based action recognition, namely Dynamic Group Spatio-Temporal GCN (DG-STGCN) It consists of two modules, DG-GCN and DG-TCN, respectively, for spatial and temporal modeling. DG-STGCN consistently outperforms state-of-the-art methods, often by a notable margin.
  arXiv  Detail & Related papers  (2022-10-12T03:17:37Z)
• Decentralized digital twins of complex dynamical systems [0.0]
  We introduce a decentralized twin (DDT) framework for dynamical systems. We discuss the prospects of the DDT paradigm in computational science and engineering applications.
  arXiv  Detail & Related papers  (2022-07-07T19:44:42Z)
• Multi-Scale Semantics-Guided Neural Networks for Efficient Skeleton-Based Human Action Recognition [140.18376685167857]
  A simple yet effective multi-scale semantics-guided neural network is proposed for skeleton-based action recognition. MS-SGN achieves the state-of-the-art performance on the NTU60, NTU120, and SYSU datasets.
  arXiv  Detail & Related papers  (2021-11-07T03:50:50Z)
• Improving Coherence and Consistency in Neural Sequence Models with Dual-System, Neuro-Symbolic Reasoning [49.6928533575956]
  We use neural inference to mediate between the neural System 1 and the logical System 2. Results in robust story generation and grounded instruction-following show that this approach can increase the coherence and accuracy of neurally-based generations.
  arXiv  Detail & Related papers  (2021-07-06T17:59:49Z)
• Co-evolution of Functional Brain Network at Multiple Scales during Early Infancy [52.4179778122852]
  This paper leveraged a longitudinal infant resting-state functional magnetic resonance imaging dataset from birth to 2 years of age. By applying our proposed methodological framework on the collected longitudinal infant dataset, we provided the first evidence that, in the first 2 years of life, the brain functional network is co-evolved at different scales.
  arXiv  Detail & Related papers  (2020-09-15T07:21:04Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.