Topology Driven Analysis of Protein - Protein Interactome for Prioritizing Key Comorbid Genes via Sub Graph Based Average Path Length Centrality

Suresh, Nikhila T; Vimina, E. R.; Krishnakumar, U.

doi:10.1109/tcbb.2022.3140388

Cited by 2 publications

(1 citation statement)

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“…Liu et al [7] introduced a collaborative clustering approach for disentangling disease heterogeneity from neuroimaging data, namely ADCoC, illustrating the potential of advanced clustering techniques in handling complex neuroimaging data. Suresh et al [8] focused on the topology-driven analysis of protein-protein interactomes, pointing out the significance of molecularlevel analyses in understanding comorbid conditions in neurodegenerative diseases. Hao et al [9] proposed a multimodal self-paced learning approach for Alzheimer's Disease diagnosis, demonstrating the efficacy of multimodal approaches in handling complex diseases.…”

Section: Literature Surveymentioning

confidence: 99%

IMNMAGN: Integrative Multimodal Approach for Enhanced Detection of Neurodegenerative Diseases Using Fusion of Multidomain Analysis With Graph Networks

Vijay Anand,

Shanmuga Priyan,

Guru Brahmam

et al. 2024

IEEE Access

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The burgeoning field of neurodegenerative disease detection and management necessitates the development of robust and comprehensive diagnostic approaches. Existing methodologies often fall short in effectively capturing the complex interplay of brain signals and genetic markers, which are crucial in the early detection and progression tracking of such diseases. This paper introduces a novel multimodal framework that leverages advanced signal processing and machine learning techniques to address these limitations, providing a more accurate and holistic understanding of neurodegenerative diseases. Our proposed model integrates multiple modalities: EEG signal analysis using Time-Frequency Analysis and Wavelet Transform, functional Magnetic Resonance Imaging (fMRI) analyzed through Independent Component Analysis (ICA) and Correlation Analysis, Magnetoencephalography (MEG) employing Beamforming and Source Localization Techniques, and Genomic Data analysis using Graph Neural Network for Genetic Pattern Recognition process. This integration is realized through the fusion of modalities using Gated Recurrent Units (GRU) and the classification into disease classes via an efficient 1D Convolutional Neural Network (CNN). The reasons for selecting these methods are twofold: they address the non-stationary characteristics of EEG signals and exploit spatial information of brain activity, while also identifying functional networks and genetic patterns associated with neurodegeneration conditions. The clinical impact of this work is profound. Tested on the BioGPS and BrainLat datasets, our framework demonstrated a 10.4% increase in precision, 8.5% increase in accuracy, 8.3% increase in recall, 9.4% increase in the Area Under the Curve (AUC), 7.5% increase in specificity, and a 2.9% reduction in delay compared to existing methods.

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Section: Literature Surveymentioning

confidence: 99%