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• W4380883362 abstract "Abstract Background The recent multi‐omics analysis explores the integration of multiple biological data types, which can suggest a more comprehensive view of biological processes underlying complex diseases, such as Alzheimer’s disease (AD). Various biological networks have been leveraged as prior knowledge with attempt to discovery of more interpretable multi‐omics markers [1]. We applied a new graph neural network to take advantage of this rich prior knowledge together with multi‐omics data for identification of system‐level AD markers. Method We used GWAS genotype, RNA‐Seq gene expression, and protein expression data from the ROS/MAP Project. In total, 133 subjects with full set of ‐omics features were included in the study (Table. 1). There are 1,751 functionally connected ‐omics features in the prior biological network, including 186 peptides, 743 unique genes, and 822 single‐nucleotide polymorphisms (SNPs). Multi‐omic network of functional connections between SNPs, genes and proteins were created based on the REACTOME database, SNP‐gene mapping relationship and the SNP2TFBS database [2]. This network was applied to guide the architecture design of the neural network. The input layer consists of all ‐omics features. A biological drop‐connect layer of Varmole was set as for transparent layer that duplicate of protein and gene nodes in the input layer [3]. Links between input layer and transparent layer were added based on the prior multi‐omic network. In addition, we also added self‐connection links for all genes and proteins. Result Our proposed integrated multi‐omic DNN largely outperforms other state‐of‐the‐art models (Table. 2). With cut‐off at 0.00001, 538 features were identified as discriminative. Three largest connected components were observed with more than 30 nodes (Fig. 1), among which 28 SNPs, 309 genes and 31 proteins form the largest connected component. Top hub nodes are proteins PIK3R1, GRB2, FYN. Shown in Fig. 2 is the list of top REACTOME pathways enriched by genes and proteins in the largest connected component [4]. Conclusion Integrated multi‐omic DNN identified multi‐mic subnetworks with great predictive power, providing critical functional connections contributing to AD. [1] Xie, et al., BIB, 2021. [2] Fabregat et al., NAR, 2018. [3] Nguyen, et al., Bioinformatics, 2021. [4] Kanehisa, et al., NAR, 2000." @default.
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• W4380883362 date "2023-06-01" @default.
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• W4380883362 title "Graph deep neural network for discovery of multi‐omic subnetworks related to Alzheimer’s Disease" @default.
• W4380883362 doi "https://doi.org/10.1002/alz.066184" @default.
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