![](https://hougroup.xyz/wp-content/uploads/2023/09/Screen-Shot-2023-09-13-at-4.55.45-PM-1024x488.png)
Overview
We are interested in modeling biological systems across different scales, providing insights into diseases mechanisms and empowering precision medicine.
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Understanding patients’ heterogeneity by joint learning of phenotypic data and oimcs data
A key challenge in addressing complex diseases lies in the considerable heterogeneity observed across patients and the temporal variability within individuals. We integrate omics data (gene expression, ATAC-seq, histone modification, DNA methylation, etc.), and medical data including medical imaging, electrical health record (EHR) to 1) investigate disease subtyping or staging and discover biomarkers; 2) prioritize potential intervention strategies for different subtypes or stages. We are particular interested in brain diseases including Alzheimer’s disease and psychiatric diseases.
![](https://hougroup.xyz/wp-content/uploads/2023/06/Direct1.png)
A multi-context view of cellular states
While a cellular state is largely defined by the differentiation stage, it is also impacted by environmental stimuli through intercellular signaling pathways. We charts a multi-context view of cellular states of different cell types by building context-specific transcription factor (TF)/enhancer/gene (TEG) networks, where different contexts include differentiation, tissue-specific microenvironment, inflammatory response, and others.
![](https://hougroup.xyz/wp-content/uploads/2023/06/Direct2.png)
Context-specific impact of genetic variants
Current efforts in the context-specific functional dissection for disease-associated genetic variants rely on joint modeling GWAS signal and available context-specific epigenomic annotations, QTL mapping or perturbation assays (CRISPR or MPRA). However, there is still a lack of studies that systematically model impact of genetic variants in TF-Enhancer-Gene (TEG) networks, and cellular states levels across conditions. We investigate the impact of genetic variants over TEG networks, the basis to infer disease-driving context-specific TEG networks.
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Project X: animal intelligence and well-being
We are currently training an animal intelligence (AI) model at home with a multi-modality (speaking, feeding, smelling, patting, … ) framework, aiming to study how pet companion impacts both mental and physical healthy longitudinally 🙂