TDCOSR: A Multimodality Fusion Framework for Association Analysis Between Genes and ROIs of Alzheimer’s Disease

Bioinformatics Research and Applications(2023)

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The complementary multimodality data fusion analysis provides a new perspective for revealing associations between genes and brain regions of interests (ROIs) of Alzheimer’s disease (AD). In this paper, we proposed a multimodality fusion framework of AD gene-ROI association analysis, named the two-stage decision cluster optimal structure reduction (TDCOSR). Specifically, gene-ROI association pairs were constructed as fusion features, where the set of candidate genes was identified from samples with only genetic modality. Second, a series of back propagation neural networks (BPNNs) were trained in parallel and then divided into multiple decision clusters. Finally, the weights of decision clusters were balanced and predict disease states by a linear stacking approach. The weights of decision clusters provide a built-in interpretation mechanism to discover fusion features with strong discriminative power. We tracked the best discriminatory features to identify disease-specific gene-ROI associations. Experiments using the TDCOSR were performed on data from the Alzheimer’s Disease Neuroimaging Initiative database, the results of which showed that the TDCOSR could identify more potential pathogenic genes and ROIs related to AD.
Alzheimer’s disease, Ensemble learning, Optimal feature extraction, Brain imaging genetics
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