Our overall research interest is to apply data science technologies to advance biomedicine and healthcare, including the creation of software tools and computational platforms to integrate multi-omics data, text-mine electronic health records, promote data annotations, and translate big data into knowledge. Recently, our projects are dedicated to explaining cardiac death with ECG-based multi-modal knowledge distillation using LLM-LM interpretation and an omics-based AI approach to elucidate molecular insights underlying pathological phenotypes in atherosclerosis.
One major effort of our team has been successfully creating best practices for trustworthy AI and computational frameworks to support biomedical dataset AI readiness, including information retrieval from knowledgebases, text mining workflows, omics data integration platforms, and trustworthy LLMs.
Another research focus is developing a hierarchical graph neural network framework to study the problem of personalized classification of subclinical atherosclerosis. We leverage two clinical aspects of a patient: clinical features (e.g., demographics, comorbidities) and molecular data (e.g., filtered omics features mapped onto patient-specific PPI subgraphs). Our aim is to enhance precision cardiovascular diagnostics and to identify disease subtypes by integrating molecular interaction signatures of each patient with clinical features that reflect cohort-level behaviors in our predictive model.
Examples of research projects are as follows.