I have the privilege of leading a multicultural and diverse multidisciplinary research group that brings together computational biologists, systems biologists, and bioinformaticians to study Alzheimer disease, related dementias and neurodegeneration. We use traditional statistical and advanced machine learning and data science approaches to analyze molecular data and provide novel insights on neurodegeneration.
Our work aims to discern how genetic factor and genes contribute to the biological processes that affect disease using multi-omic integrative approaches with particular emphases in single-cell molecular data and spatially resolved transcriptomics. We develop and use innovative analytical workflows to study the genetic, transcriptomic, proteomic, metabolomic, and epigenetic data from tissue homogenates and single-cell brain tissue.

Single-cell genomics: It has emerged as a powerful approach to interrogate the underlying genomic or transcriptomic landscape of the cellularly-complex human brain. We are currently using single nuclei RNA-seq (snRNA-seq), single nuclei epigenetics (snATACseq) and spatially resolved transcriptomics coupled with genetic data to determine cell-type differences and transcriptional states in Alzheimer’s disease (AD) and Autosomal-Dominant AD (ADAD), related dementias and neurodegeneration.

High-throughput and Cross-omics: We use a machine learning framework to integrate heterogeneous molecular data from multiple human brain regions and cohorts. This enables us to leverage high dimensional analytics to study molecular profile changes associated with disease, and also uncover the relationships and correlations between different biological molecules.

Machine Learning: We leverage latest deep learning and traditional supervised/unsupervised learning methods to infer from high dimensional molecular data key hypotheses that we further validate in functional studies through ongoing projects with our collaborators.

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