A5094055197- Single-cell and spatial transcriptomics
- Cancer-related molecular mechanisms research
- Gene Regulatory Network Analysis
- Molecular Biology Techniques and Applications
- Bioinformatics and Genomic Networks
- Rough Sets and Fuzzy Logic
- Geochemistry and Geologic Mapping
- Multi-Criteria Decision Making
- Gene expression and cancer classification
BGI Group (China)
2024
BGI Research
2024
University of Chinese Academy of Sciences
2024
10X Genomics (United States)
2023
Northwest A&F University
2020
Abstract Background The emergence of high-resolved spatial transcriptomics (ST) has facilitated the research novel methods to investigate biological development, organism growth, and other complex processes. However, whole ST datasets require customized imputation improve signal-to-noise ratio data quality. Findings We propose an efficient adaptive Gaussian smoothing (EAGS) method for ST. 2-factor EAGS creates patterns based on expression information cells, weights cells in same pattern,...
The regulation of gene expression in plants is governed by complex interactions between cis-regulatory elements and epigenetic modifications such as histone marks. While deep learning models have achieved success predicting regulatory features from DNA sequence, their cross-species generalizability remains largely unexplored. In this study, we systematically evaluated the ability to predict across plant species using a multi-stage framework based on Sei architecture. We trained...
Single-cell multi-omics integration enables joint analysis at the single-cell level of resolution to provide more accurate understanding complex biological systems, while spatial is benefit exploration cell heterogeneity facilitate comprehensive downstream analyses. Existing methods are mainly designed for data with little consideration information and still have room performance improvement. A reliable method both spatially resolved necessary significant. We propose a based on dual-path...
Abstract Single-cell multi-omics data integration enables joint analysis of the resolution at single-cell level to provide comprehensive and accurate understanding complex biological systems, while spatial is benefit exploration cell heterogeneity facilitate more diversified downstream analyses. Existing methods are mainly designed for with little consideration on information, still have room performance improvement. A reliable method that can be applied both spatially resolved necessary...