Zobrazeno 1 - 8
of 8
pro vyhledávání: '"Minsheng Hao"'
Autor:
Minsheng Hao, Erpai Luo, Yixin Chen, Yanhong Wu, Chen Li, Sijie Chen, Haoxiang Gao, Haiyang Bian, Jin Gu, Lei Wei, Xuegong Zhang
Publikováno v:
Communications Biology, Vol 7, Iss 1, Pp 1-15 (2024)
Abstract Profiling spatial variations of cellular composition and transcriptomic characteristics is important for understanding the physiology and pathology of tissues. Spatial transcriptomics (ST) data depict spatial gene expression but the currentl
Externí odkaz:
https://doaj.org/article/cc349d03ad9247699c7dbea77f5aecb2
Autor:
Yixin Chen, Minsheng Hao, Haoxiang Gao, Jiaqi Li, Sijie Chen, Fanhong Li, Lei Wei, Xuegong Zhang
Publikováno v:
STAR Protocols, Vol 3, Iss 3, Pp 101589- (2022)
Summary: Human Ensemble Cell Atlas (hECA) provides a unified informatics framework and the cell-centric-assembled single-cell transcriptome data of 1,093,299 labeled human cells from 116 published datasets. In this protocol, we provide three applicat
Externí odkaz:
https://doaj.org/article/8d56691db9e040a8bc80cd3f9370fde0
Autor:
Sijie Chen, Yanting Luo, Haoxiang Gao, Fanhong Li, Yixin Chen, Jiaqi Li, Renke You, Minsheng Hao, Haiyang Bian, Xi Xi, Wenrui Li, Weiyu Li, Mingli Ye, Qiuchen Meng, Ziheng Zou, Chen Li, Haochen Li, Yangyuan Zhang, Yanfei Cui, Lei Wei, Fufeng Chen, Xiaowo Wang, Hairong Lv, Kui Hua, Rui Jiang, Xuegong Zhang
Publikováno v:
iScience, Vol 25, Iss 5, Pp 104318- (2022)
Summary: The accumulation of massive single-cell omics data provides growing resources for building biomolecular atlases of all cells of human organs or the whole body. The true assembly of a cell atlas should be cell-centric rather than file-centric
Externí odkaz:
https://doaj.org/article/4443245c3093416e8c6100e8588b9049
Autor:
Minsheng Hao, Jing Gong, Xin Zeng, Chiming Liu, Yucheng Guo, Xingyi Cheng, Taifeng Wang, Jianzhu Ma, Le Song, Xuegong Zhang
Large-scale pretrained models have become foundation models, leading to breakthroughs in natural language processing and related fields. Developing foundation models in life science, aimed at deciphering the "languages" of cells and facilitating biom
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::347bf5069b4088a9ab60cf4fbc21699e
https://doi.org/10.1101/2023.05.29.542705
https://doi.org/10.1101/2023.05.29.542705
Autor:
Jing Gong, Minsheng Hao, Xin Zeng, Chiming Liu, Jianzhu Ma, Xingyi Cheng, Taifeng Wang, Xuegong Zhang, Le Song
The advances in high-throughput sequencing technology have led to significant progress in measuring gene expressions in single-cell level. The amount of publicly available single-cell RNA-seq (scRNA-seq) data is already surpassing 50M records for hum
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::7f43d0db152fc19fa8cad1e80600682b
https://doi.org/10.1101/2023.03.24.534055
https://doi.org/10.1101/2023.03.24.534055
Computationally integrating spatial transcriptomics (ST) and single-cell transcriptomics (SC) greatly benefits biomedical research such as cellular organization, embryogenesis and tumorigenesis, and could further facilitate therapeutic developments.
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::9884b916431e702a1cb37326174fc017
https://doi.org/10.1101/2022.09.23.509186
https://doi.org/10.1101/2022.09.23.509186
Autor:
Haoxiang Gao, Xiaowo Wang, Kui Hua, Yixin Chen, Ziheng Zou, Weiyu Li, Renke You, Xi Xi, Lei Wei, Fufeng Chen, Qiuchen Meng, Wenrui Li, Hairong Lv, Yanfei Cui, Chen Li, Xuegong Zhang, Minsheng Hao, Sijie Chen, Mingli Ye, Rui Jiang, Yangyuan Zhang, Fanhong Li, Jiaqi Li, Haiyang Bian, Haochen Li, Yanting Luo
Publikováno v:
iScience. 25(5)
SUMMARYSingle-cell omics data can characterize multifaceted features of massive cells and bring significant insights to biomedical researches. The accumulation of single-cell data provides growing resources for constructing atlases for all cells of a
Publikováno v:
Bioinformatics (Oxford, England).
Motivation Recent developments of spatial transcriptomic sequencing technologies provide powerful tools for understanding cells in the physical context of tissue microenvironments. A fundamental task in spatial gene expression analysis is to identify