Multi-omic single-cell, electronic health record, and biomedical knowledge graph data integration using interpretable deep learning approaches

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As electronic health record (EHR) and genomic technologies such as single-cell multi-omic sequencing becoming mature, we are on the verge of convergence in terms of using one type of data (e.g., single-cell genomic) to understand the other and vice versa. Multi-modal data integration of the single-cell genomic and heterogeneous EHR data will lead to new mechanistic insights into the human complex phenotypes. The objective of the proposed international collaboration is to build a machine learning (ML) method to integrate EHR and genomic data. Our approach will address missing data problem that are common in data integration from multiple studies by leveraging existing comprehensively curated biomedical knowledge graph from large systems like the Universal Medical Language System (UMLS), Gene Ontology (GO), String database, which consists of known inter-network and intra-network connections for the phenotype and gene vertices. By using a deep learning approach, we will compute for each gene or each phenotype a numerical vector from the knowledge graph, which can then be used to model the actual EHR and gene expression data. We will represent these numerical vectors by their topic mixture memberships for a finite set of latent topic distributions over the genes and phenotypes. Examining top genes and phenotypes with high probabilities under the same topic will reveal gene regulatory network modules that govern the phenotypic comorbidities. Furthermore, we will account for multi-modal data distribution and confounding effects when integrating data from multiple sources. The collaboration will combine domain expertise from Dr. Fei Wang from Cornell University and Dr. Yue Li from McGill University in tackling the highly interdisciplinary research project, bridging gaps across disciplines spanning across ML, genomics, and health informatics. We will build an intelligent digital platform that enables researchers and health-related practitioners to not only explore plausible phenotypic and genomic connections but also integrate their own data. The success of the project will accelerate the paradigm shift of integrative analytical frameworks using ML approaches in Canada.