<p dir="ltr">Multi-omic studies ranging from genomics, transcriptomics (e.g., gene expression) to proteomics data exploration have been widely applied to interpret findings from genome wide association studies (GWAS) of Alzheimer's disease (AD). However, previous studies examine each -omics data type individually and the functional interactions between genetic variations, genes and proteins are only used after discovery to interpret the findings, but not beforehand. In this case, multi-omic findings are likely not functionally related and therefore it is challenging for result interpretation. To handle this challenge, we present new modularity constrained least absolute shrinkage and selection operator (M-LASSO), new modularity constrained logistic regression (M-Logistic), new interpretable multi-omic graph fusion neural network model (MoFNet) and new transfer learning framework integrated graph fusion neural network model (TransFuse) to integrate prior biological knowledge to model the functional interactions of multi-omic data. These approaches aim to identify functional connected sub-networks predictive of AD. In this thesis, the intrepretable model MoFNet and TransFuse incorporate prior biological connected multi-omics network, and for the first time model the dynamic information flow from deoxyribonucleic acid (DNA) to ribonucleic acid (RNA) and proteins. While applying the proposed models on multi-omic data from the religious orders study/memory and aging project (ROS/MAP) cohort, MoFNet and TransFuse outperformed all other state-of-art classifiers. Instead of targeting individual markers, the proposed methods identified multi-omic sub-networks associated with AD. MoFNet and TransFuse, produced sub-network and pathway findings that were robustly validated in another independent cohort. These identified gene/protein networks highlight potential pathways involved in AD pathogenesis and could offer systematic overview for understanding the molecular mechanisms of the disease. Investigating these identified pathways in more detail could help uncover the mechanisms causing synaptic dysfunction in AD and guide future research into potential therapeutic targets.</p>
| Identifer | oai:union.ndltd.org:purdue.edu/oai:figshare.com:article/26332633 |
| Date | 03 September 2024 |
| Creators | Linhui Xie (19175077) |
| Source Sets | Purdue University |
| Detected Language | English |
| Type | Text, Thesis |
| Rights | CC BY 4.0 |
| Relation | https://figshare.com/articles/thesis/Unraveling_the_Multi-omic_Network_and_Pathway_Alterations_in_Alzheimer_s_Disease/26332633 |
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