Graph Pseudotime Analysis and Neural Stochastic Differential Equations for Analyzing Retinal Degeneration Dynamics and Beyond

• URL: http://arxiv.org/abs/2502.06126v1
• Date: Mon, 10 Feb 2025 03:25:14 GMT
• Title: Graph Pseudotime Analysis and Neural Stochastic Differential Equations for Analyzing Retinal Degeneration Dynamics and Beyond
• Authors: Dai Shi, Kuan Yan, Lequan Lin, Yue Zeng, Ting Zhang, Dmytro Matsypura, Mark C. Gillies, Ling Zhu, Junbin Gao,
• Abstract summary: We develop a method to efficiently construct pathway graphs for subjects from our newly curated JR5558 mouse transcriptomics dataset.<n>We then develop Graph-level Pseudotime Analysis (GPA) to infer graph-level trajectories that reveal how disease progresses at the population level.<n>Based on GPA, we identify the most sensitive pathways that drive disease stage transitions.<n>We extend our theory to the case when pathways can interact with each other, enabling a more comprehensive and multi-faceted characterization of disease phenotypes.
• Score: 23.5532897473393
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Understanding disease progression at the molecular pathway level usually requires capturing both structural dependencies between pathways and the temporal dynamics of disease evolution. In this work, we solve the former challenge by developing a biologically informed graph-forming method to efficiently construct pathway graphs for subjects from our newly curated JR5558 mouse transcriptomics dataset. We then develop Graph-level Pseudotime Analysis (GPA) to infer graph-level trajectories that reveal how disease progresses at the population level, rather than in individual subjects. Based on the trajectories estimated by GPA, we identify the most sensitive pathways that drive disease stage transitions. In addition, we measure changes in pathway features using neural stochastic differential equations (SDEs), which enables us to formally define and compute pathway stability and disease bifurcation points (points of no return), two fundamental problems in disease progression research. We further extend our theory to the case when pathways can interact with each other, enabling a more comprehensive and multi-faceted characterization of disease phenotypes. The comprehensive experimental results demonstrate the effectiveness of our framework in reconstructing the dynamics of the pathway, identifying critical transitions, and providing novel insights into the mechanistic understanding of disease evolution.

Related papers

• LLM enhanced graph inference for long-term disease progression modelling [11.745038114408395]
  We present a novel framework that uses Large Language Models (MsLL) as expert guides on the interaction of regional variables to enhance learning of disease progression.<n>We demonstrate the new approach by estimating the pathology propagation using tau-PET imaging data from an Alzheimer's disease cohort.
  arXiv  Detail & Related papers  (2025-11-14T02:03:10Z)
• Conditional Neural ODE for Longitudinal Parkinson's Disease Progression Forecasting [51.906871559732245]
  Parkinson's disease (PD) shows heterogeneous, evolving brain-morphometry patterns.<n>Modeling these longitudinal trajectories enables mechanistic insight, treatment development, and individualized 'digital-twin' forecasting.<n>We propose CNODE, a novel framework for continuous, individualized PD progression forecasting.
  arXiv  Detail & Related papers  (2025-11-06T20:16:33Z)
• Uncovering Alzheimer's Disease Progression via SDE-based Spatio-Temporal Graph Deep Learning on Longitudinal Brain Networks [18.54013631358448]
  We develop a neural network framework to predict future Alzheimer's disease (AD) progression.<n>Our framework effectively learns sparse regional and connective importance probabilities.<n>Our findings highlight the potential of-temporal graph-based learning for early, individualized prediction of AD progression.
  arXiv  Detail & Related papers  (2025-09-26T01:02:34Z)
• A Stage-Aware Mixture of Experts Framework for Neurodegenerative Disease Progression Modelling [6.577465893404979]
  Long-term progression of neurodegenerative diseases is commonly conceptualized as a graph diffusion process.<n>We propose a novel stage-aware Mixture Experts framework that explicitly models how different contributing mechanisms dominate at different disease stages.<n>The resulting IGND-MoE model dynamically integrates these components across temporal states, offering a principled way to understand how stage-specific pathological mechanisms contribute to progression.
  arXiv  Detail & Related papers  (2025-08-09T16:09:00Z)
• Parkinson's Disease Detection from Resting State EEG using Multi-Head Graph Structure Learning with Gradient Weighted Graph Attention Explanations [9.544065991313062]
  We propose a novel graph neural network (GNN) technique for explainable Parkinson's disease (PD) detection using resting state EEG. We employ structured global convolutions with contrastive learning to better model complex features with limited data. We developed and evaluated our method using the UC San Diego Parkinson's disease EEG dataset, and achieved 69.40% detection accuracy in subject-wise leave-one-out cross-validation.
  arXiv  Detail & Related papers  (2024-08-01T20:54:33Z)
• GTP-4o: Modality-prompted Heterogeneous Graph Learning for Omni-modal Biomedical Representation [68.63955715643974]
  Modality-prompted Heterogeneous Graph for Omnimodal Learning (GTP-4o) We propose an innovative Modality-prompted Heterogeneous Graph for Omnimodal Learning (GTP-4o)
  arXiv  Detail & Related papers  (2024-07-08T01:06:13Z)
• Highly Accurate Disease Diagnosis and Highly Reproducible Biomarker Identification with PathFormer [32.26944736442376]
  Graph neural networks (GNNs) have been the dominant deep learning model for analyzing graph-structured data. The root of the challenges is the unique graph structure of biological signaling pathways. We present a novel GNN model architecture, named PathFormer, which integrates signaling network, priori knowledge and omics data to rank biomarkers and predict disease diagnosis.
  arXiv  Detail & Related papers  (2024-02-11T18:23:54Z)
• Classification of developmental and brain disorders via graph convolutional aggregation [6.6356049194991815]
  We introduce an aggregator normalization graph convolutional network by leveraging aggregation in graph sampling. The proposed model learns discriminative graph node representations by incorporating both imaging and non-imaging features into the graph nodes and edges. We benchmark our model against several recent baseline methods on two large datasets, Autism Brain Imaging Data Exchange (ABIDE) and Alzheimer's Disease Neuroimaging Initiative (ADNI)
  arXiv  Detail & Related papers  (2023-11-13T14:36:29Z)
• Pathology Steered Stratification Network for Subtype Identification in Alzheimer's Disease [7.594681424335177]
  Alzheimers disease (AD) is a heterogeneous, multitemporal neurodegenerative disorder characterized by beta-amyloid, pathologic tau, and neurodegeneration. We propose a novel pathology steered stratification network (PSSN) that incorporates established domain knowledge in AD pathology through a reaction-diffusion model.
  arXiv  Detail & Related papers  (2022-10-12T02:52:00Z)
• Graph-Evolving Meta-Learning for Low-Resource Medical Dialogue Generation [150.52617238140868]
  We propose low-resource medical dialogue generation to transfer the diagnostic experience from source diseases to target ones. We also develop a Graph-Evolving Meta-Learning framework that learns to evolve the commonsense graph for reasoning disease-symptom correlations in a new disease.
  arXiv  Detail & Related papers  (2020-12-22T13:20:23Z)
• Trajectories, bifurcations and pseudotime in large clinical datasets: applications to myocardial infarction and diabetes data [94.37521840642141]
  We suggest a semi-supervised methodology for the analysis of large clinical datasets, characterized by mixed data types and missing values. The methodology is based on application of elastic principal graphs which can address simultaneously the tasks of dimensionality reduction, data visualization, clustering, feature selection and quantifying the geodesic distances (pseudotime) in partially ordered sequences of observations.
  arXiv  Detail & Related papers  (2020-07-07T21:04:55Z)
• Finding Patient Zero: Learning Contagion Source with Graph Neural Networks [67.3415507211942]
  Locating the source of an epidemic can provide critical insights into the infection's transmission course. Existing methods use graph-theoretic measures and expensive message-passing algorithms. We revisit this problem using graph neural networks (GNNs) to learn P0.
  arXiv  Detail & Related papers  (2020-06-21T21:12:44Z)
• A Graph Gaussian Embedding Method for Predicting Alzheimer's Disease Progression with MEG Brain Networks [59.15734147867412]
  Characterizing the subtle changes of functional brain networks associated with Alzheimer's disease (AD) is important for early diagnosis and prediction of disease progression. We developed a new deep learning method, termed multiple graph Gaussian embedding model (MG2G) We used MG2G to detect the intrinsic latent dimensionality of MEG brain networks, predict the progression of patients with mild cognitive impairment (MCI) to AD, and identify brain regions with network alterations related to MCI.
  arXiv  Detail & Related papers  (2020-05-08T02:29:24Z)
• Learning Dynamic and Personalized Comorbidity Networks from Event Data using Deep Diffusion Processes [102.02672176520382]
  Comorbid diseases co-occur and progress via complex temporal patterns that vary among individuals. In electronic health records we can observe the different diseases a patient has, but can only infer the temporal relationship between each co-morbid condition. We develop deep diffusion processes to model "dynamic comorbidity networks"
  arXiv  Detail & Related papers  (2020-01-08T15:47:08Z)

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.