AI models of multi-omic data integration for ming longevity core signaling pathways

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PROJECT SUMMARY Exceptional longevity (EL) is strongly correlated with exceptional health span, lower risk and delayed onset of age-related diseases. Moreover, EL is a complex genetic trait, like aging-related diseases, affected by polygenic targets, and other factors, like sex, ethnicity, lifestyle choices, social and environmental factors. Thus, in EL studies, single protective genetic targets usually have weaker effects upon survival to extreme age. Whereas, the right combination of genetic targets, as well as other factors, can have a stronger effect. Therefore, it is important to discover these protective factors, genetic targets and subsequent signaling pathways of EL, which are the critical basis to guide the development of novel medications and management for disease prevention/treatment to extend health and life span. Large-scale and multi-omics datasets, like genome, epigenome, transcriptome, proteome, metabolome, microbiome, phenome, of large-scale cohorts of centenarians and exceptional long-lived individuals, have been being generated in multiple EL projects. Whereas, it remains challenging to integrate and interpret complex multi-omics datasets. In response to the NIH RFA-AG-23-033, we propose to improve and develop novel artificial intelligence (AI) models that can efficiently integrate and interpret the EL multi-omics datasets, and identify risk and protective targets and medications to correct the disease risk signaling pathways for disease prevention and long and healthy life span extension. Deep learning (DL) and AI models have been widely used in the healthcare field and outperform traditional machine learning models, and thus offering solutions to this critical problem. We have rich experience in developing interpretable AI models of multi-omics data analysis for target ranking and core signaling network inference. In this study, we will (Aim 1) develop two (GNN) AI models, PathFormer and PathFinder, for unbiased core signaling pathways inference using multi-omics data (unbiased/unguided inference); (aim 2): develop a novel GNN AI model, modular k-Hop DeepNetFlow, for hypothesis guided core signaling pathway inference using multi-omics data (semi-guided inference); (Aim 4): develop novel DeepDrugMap knowledge graph, and Knowledge-driven, Multi-Module, Multi-Evidence (M3E) models to predict drugs that can boost protective signaling and inhibit the risk signaling pathways for disease prevention/treatment; develop a novel, open-source visual programming tool, LongevityOmicNet, to support the dissemination and reproducible analysis of the AI models with diverse supportive datasets, to the broader EL or aging study community. Also (Aim 3): collaborating with Dr. Michael Province (Co-PI), leading the LLFS project in WashU, we will apply these AI models to identify EL-associated protective factors, like the Sex, Genetics, Insulin resistance, Environment factors (SGIE-factors), and associated signaling pathways/biological processes, using large-scale multi-omics data of EL studies, i.e., LLFS, LG and ILO studies.