Development of a structured knowledge graph for AD for better prediction, diagnosis and treatment

We will use the data to build an AD-related omics database both by using state-of-the-art and by developing advanced deep learning-based methods for harmonization and imputation of multi-omics data, facilitating system biology studies to gain deep understanding of AD.

We will use the data to identify the genetic biomarkers with causal effect on behavioral deficits in Alzheimer’s study and use these biomarkers to help predict Alzheimer's disease (AD). SNP data will be screened first using the GWAS and significant SNPs will be selected as genetic biomarkers according to their p-values. Then causal effects will be estimated to evaluate the contribution of genetic biomarkers. For AD prediction, enhanced statistical, machine learning, and deep learning approaches will be explored and compared, which may include but not limited to: the PCA decomposition, ridge regression/elastic net algorithm, boosting algorithms such as XGBoost/lightGBM, deep learning models such as the deep factorization machine.

In far future, we want to develop a structured, literature and expert-knowledge based knowledge graph for better prediction, diagnosis and treatment of AD.

Leveraging our newly developed causal inference method, we aim to identify genetic causal pathways for the Alzheimer's disease (AD) from genomic data collected from multiple populations. The identified features will be used to predict cognitive and behavior scores among patients with AD or mild cognitive impairment (MCI). Finally, we want to develop a knowledge graph for better prediction, diagnosis and treatment of AD.