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Updated: Sep 20, 2025

A Knowledge Graph Approach to Elucidate the Role of Organellar Pathways in Disease via Biomedical Reports
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Combining Neuroimaging and Omics Datasets for Disease Classification Using Graph Neural Networks.

Yi Hao Chan1, Conghao Wang1, Wei Kwek Soh1

  • 1School of Computer Science and Engineering, Nanyang Technological University, Singapore, Singapore.

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|June 9, 2022
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Summary

This study introduces a deep learning model integrating brain imaging and multi-omics data for neurodegenerative disease classification. The model effectively identifies Parkinson's disease, highlighting the importance of DNA Methylation and SNP data.

Keywords:
Generative Adversarial NetworksParkinson's diseaseattentiondiffusion tensor imagingdisease classificationfunctional magnetic resonance imaginggraph convolutional networksmulti-omics

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Area of Science:

  • Neuroscience and Computational Biology
  • Integrative multi-omics and neuroimaging analysis for disease classification

Background:

  • Neurodegenerative diseases like Parkinson's involve complex changes in both brain structure/function and genetic/molecular profiles.
  • Integrating high-dimensional neuroimaging (fMRI, DTI) and multi-omics data presents significant computational challenges.
  • Existing methods often struggle to effectively combine multi-modal imaging and multi-omics data for robust neurological insights.

Purpose of the Study:

  • To develop a novel deep neural network architecture for classifying neurodegenerative diseases by integrating multi-modal imaging and multi-omics data.
  • To identify key imaging and omics features contributing to disease classification, specifically for Parkinson's disease (PD).
  • To address data challenges such as missing modalities and class imbalance in datasets like the Parkinson's Progression Markers Initiative (PPMI).

Main Methods:

  • Proposed a deep neural network utilizing graph convolution layers to model functional magnetic resonance imaging (fMRI) and diffusion tensor imaging (DTI) connectome data.
  • Incorporated multi-omics data (RNA Expression, SNP, DNA Methylation, non-coding RNA) using separate graph convolution layers, forming population graphs.
  • Employed an attention mechanism for fusing connectome and omics representations, enabling insight into modality contributions; compared oversampling with CycleGAN for data augmentation.

Main Results:

  • Achieved a Matthew Correlation Coefficient greater than 0.8 in Parkinson's disease classification using combined multi-modal imaging and multi-omics data.
  • Attention mechanism analysis identified DNA Methylation and Single Nucleotide Polymorphism (SNP) data as the most influential omics modalities for PD prediction.
  • Demonstrated the effectiveness of CycleGAN for generating missing imaging modalities to handle data scarcity and imbalance.

Conclusions:

  • The proposed deep learning architecture effectively integrates multi-modal imaging and multi-omics data for accurate neurodegenerative disease classification.
  • Highlights the critical role of DNA Methylation and SNP data in understanding the genetic underpinnings of Parkinson's disease.
  • Motivates further research in imaging genetics and the development of comprehensive multi-modal datasets for studying complex neurological disorders.