Article

Motif-induced Subgraph Generative Learning for Explainable Neurological Disorder Detection

2024-10-29

Abstract excerpt

The wide variation in symptoms of neurological disorders among patients necessitates uncovering individual pathologies for accurate clinical diagnosis and treatment. Current methods attempt to generalize specific biomarkers to explain individual pathology, but they often lack analysis of the underlying pathogenic mechanisms, leading to biased biomarkers and unreliable diagnoses. To address this issue, we propose a...

Topics

Open a Topic to create a Post that cites this publication.

Identifiers and source

Literature Corpus work: 0f2b5692-fb08-510f-b1f7-e284cc10eac6
DOI: 10.1101/2024.10.27.24316244

Open publication

Related research

Semantic proximity does not establish scientific evidence.

Click a neighbor to travelStep 1 · 12 closest

Motif-induced Subgraph Generative Learning for Explainable Neurological Disorder DetectionDOI 10.1101/2024.10.27.24316244Read

HieMaGT: Hierarchical Multi-Scale Graph Transformer for Brain Disorder DiagnosisSemantic proximity Read

Multimodal image fusion via deep generative modelsSemantic proximity Read

Individual-First Brain State Modeling Reconciles Personalized Prediction with Generalizable Mechanistic Interpretation in Precision PsychiatrySemantic proximity Read

Non-negative matrix factorization of multimodal MRI, fMRI and phenotypic data reveals differential changes in default mode subnetworks in ADHD.Semantic proximity Read

DLNDD: An Explainable Deep Learning Framework for the Early DetectionSemantic proximity Read

GNN-SubNet: disease subnetwork detection with explainable Graph Neural NetworksSemantic proximity Read

Structural Connectome Analysis using a Graph-based Deep Model for Prediction of Non-Imaging VariablesSemantic proximity Read

Combining Neuroimaging and Omics Datasets for Disease Classification Using Graph Neural NetworksSemantic proximity Read

mosGraphGPT: a foundation model for multi-omic signaling graphs using generative AISemantic proximity Read

A Biologically Interpretable Graph Convolutional Network to Link Genetic Risk Pathways and Neuroimaging Markers of DiseaseSemantic proximity Read

Conditioned Graph Reconstruction of Brain Functional Network Connectivity Reveals Interpretable Latent Axes of Sex and Fluid IntelligenceSemantic proximity Read

Gene-SGAN: discovering disease subtypes with imaging and genetic signatures via multi-view weakly-supervised deep clusteringSemantic proximity Read