A5059968722- Single-cell and spatial transcriptomics
- RNA Research and Splicing
- Gene Regulatory Network Analysis
- Cell Image Analysis Techniques
- Metallurgy and Material Forming
- Adipose Tissue and Metabolism
- Molecular Biology Techniques and Applications
- Cancer-related molecular mechanisms research
- Cancer Genomics and Diagnostics
- Manufacturing Process and Optimization
- Bioinformatics and Genomic Networks
- Gene expression and cancer classification
- Scheduling and Optimization Algorithms
- Hydrocarbon exploration and reservoir analysis
- CRISPR and Genetic Engineering
Yale University
2024
Hong Kong University of Science and Technology
2022-2024
University of Hong Kong
2022-2024
Stanford University
2021
ABSTRACT Mouse lemurs are the smallest, fastest reproducing, and among most abundant primates, an emerging model organism for primate biology, behavior, health conservation. Although much has been learned about their physiology Madagascar ecology phylogeny, little is known cellular molecular biology. Here we used droplet- plate-based single cell RNA-sequencing to profile 226,000 cells from 27 mouse lemur organs tissues opportunistically procured four donors clinically histologically...
Recent advancements in single-cell technologies have enabled comprehensive characterization of cellular states through transcriptomic, epigenomic, and proteomic profiling at resolution. These significantly deepened our understanding cell functions disease mechanisms from various omics perspectives. As these evolve rapidly data resources expand, there is a growing need for computational methods that can integrate information different modalities to facilitate joint analysis multi-omics data....
Recent advances in spatial transcriptomics technologies have led to a growing number of diverse datasets, offering unprecedented opportunities explore tissue organizations and functions within contexts. However, it remains significant challenge effectively integrate interpret these data, often originating from different samples, technologies, developmental stages. In this paper, we present INSPIRE, deep learning method for integrative analyses multiple datasets address challenge. With...
Abstract Background Mild Cognitive Impairment (MCI) is considered as a transitional state between age‐related cognitive decline and dementia. Accurate prediction of those at risk MCI important for timely intervention treatment Alzheimer’s disease. In this study, we show that incorporating the National Coordinating Center (NACC) Uniform Data Set (UDS) with rich resources from Electronic Health Records (EHR), including comorbidities medication histories, can achieve higher accuracy, compared...
Abstract Understanding cellular responses to genetic perturbations is essential for understanding gene regulation and phenotype formation. While high-throughput single-cell RNA-sequencing has facilitated detailed profiling of heterogeneous transcriptional at the level, there remains a pressing need computational models that can decode mechanisms driving these accurately predict outcomes prioritize target genes experimental design. Here, we present scLAMBDA, deep generative learning framework...