日期:
2020 年 — 2026 年
2020
2021
2022
2023
2024
2025
2026
影响因子:

Multiscale confidence quantification for virtual spatial transcriptomics with UTOPIA

利用 UTOPIA 进行虚拟空间转录组学的多尺度置信度量化

Jin, Kaitian; Chen, Zihao; Yu, Xiaokang; Yuan, Musu; Schroeder, Amelia; Dumoulin, Bernhard; Liu, Yunhe; Wang, Linghua; Park, Jeong Hwan; Hwang, Tae Hyun; Susztak, Katalin; Ren, Zhimei; Zhang, Nancy R; Li, Mingyao

Toward Computationally Complete Spatial Omics

迈向计算完整的空间组学

Li, Wei; Mao, Liran; Liu, Yunhe; Peng, Fuduan; Sachs, Nadja; Wu, Wenrui; Yiu, Stephanie Pei Tung; Yan, Hanying; Schroeder, Amelia; Yu, Xiaokang; Jin, Kaitian; Jiang, Shunzhou; Chen, Zihao; Loth, Melanie L; Gomez, Lorena; Lubo, Idania; Blank, Niklas; Samarah, Laith Z; Basak, Ankit; Cho, Ye Won; Chen, Chia-Yu; Kim, David M; Shalek, Alex K; Soto, Luisa Maren Solis; Rabinowitz, Joshua D; Reilly, Muredach P; Qian, Xuyu; Thaiss, Christoph A; Maegdefessel, Lars; Wang, Linghua; Kadara, Humam; Jiang, Sizun; Deng, Yanxiang; Li, Mingyao

HistoSweep enables cellular-resolution tissue quality control for gigapixel images in digital pathology and spatial omics

HistoSweep 可对数字病理学和空间组学中的千兆像素图像进行细胞分辨率的组织质量控制。

Schroeder, Amelia; Yu, Xiaokang; Li, Wei; Yuan, Musu; Mao, Liran; Yang, Jiyuan; Sachs, Nadja; Dumoulin, Bernhard; Xu, George X; Luo, Xunda; Huang, Alexander; Susztak, Katalin; Hwang, Tae Hyun; Kadara, Humam; Maegdefessel, Lars; Yu, Jiyang; Li, Mingyao

Scaling up spatial transcriptomics for large-sized tissues: uncovering cellular-level tissue architecture beyond conventional platforms with iSCALE

利用 iSCALE 将空间转录组学扩展到大尺寸组织:揭示超越传统平台的细胞级组织结构

Schroeder, Amelia; Loth, Melanie L; Luo, Chunyu; Yao, Sicong; Yan, Hanying; Zhang, Daiwei; Piya, Sarbottam; Plowey, Edward; Hu, Wenxing; Clemenceau, Jean R; Jang, Inyeop; Kim, Minji; Barnfather, Isabel; Chan, Su Jing; Reynolds, Taylor L; Carlile, Thomas; Cullen, Patrick; Sung, Ji-Youn; Tsai, Hui-Hsin; Park, Jeong Hwan; Hwang, Tae Hyun; Zhang, Baohong; Li, Mingyao

Smart spatial omics (S2-omics) optimizes region of interest selection to capture molecular heterogeneity in diverse tissues

智能空间组学(S2-omics)优化感兴趣区域的选择,以捕获不同组织中的分子异质性。

Yuan, Musu; Jin, Kaitian; Yan, Hanying; Schroeder, Amelia; Luo, Chunyu; Yao, Sicong; Dumoulin, Bernhard; Levinsohn, Jonathan; Luo, Tianhao; Clemenceau, Jean R; Jang, Inyeop; Kim, Minji; Liu, Yunhe; Deng, Minghua; Furth, Emma E; Wilson, Parker; Nayak, Anupma; Lubo, Idania; Solis Soto, Luisa Maren; Wang, Linghua; Park, Jeong Hwan; Susztak, Katalin; Hwang, Tae Hyun; Li, Mingyao

Designing smart spatial omics experiments with S2Omics

利用S2Omics设计智能空间组学实验

Yuan, Musu; Jin, Kaitian; Yan, Hanying; Schroeder, Amelia; Luo, Chunyu; Yao, Sicong; Domoulin, Bernhard; Levinsohn, Jonathan; Luo, Tianhao; Clemenceau, Jean R; Jang, Inyeop; Kim, Minji; Liu, Yunhe; Deng, Minghua; Furth, Emma E; Wilson, Parker; Nayak, Anupma; Lubo, Idania; Soto, Luisa Maren Solis; Wang, Linghua; Park, Jeong Hwan; Susztak, Katalin; Hwang, Tae Hyun; Li, Mingyao

Scaling up spatial transcriptomics for large-sized tissues: uncovering cellular-level tissue architecture beyond conventional platforms with iSCALE.

利用 iSCALE 将空间转录组学扩展到大组织:揭示超越传统平台的细胞级组织结构

Schroeder Amelia, Loth Melanie, Luo Chunyu, Yao Sicong, Yan Hanying, Zhang Daiwei, Piya Sarbottam, Plowey Edward, Hu Wenxing, Clemenceau Jean R, Jang Inyeop, Kim Minji, Barnfather Isabel, Chan Su Jing, Reynolds Taylor L, Carlile Thomas, Cullen Patrick, Sung Ji-Youn, Tsai Hui-Hsin, Park Jeong Hwan, Hwang Tae Hyun, Zhang Baohong, Li Mingyao

Inferring super-resolution tissue architecture by integrating spatial transcriptomics with histology

通过将空间转录组学与组织学相结合来推断超分辨率组织结构

Zhang, Daiwei; Schroeder, Amelia; Yan, Hanying; Yang, Haochen; Hu, Jian; Lee, Michelle Y Y; Cho, Kyung S; Susztak, Katalin; Xu, George X; Feldman, Michael D; Lee, Edward B; Furth, Emma E; Wang, Linghua; Li, Mingyao

Unlocking the power of spatial omics with AI

利用人工智能释放空间组学的潜力

Coleman, Kyle; Schroeder, Amelia; Li, Mingyao

Developing a low-cost, open-source, locally manufactured workstation and computational pipeline for automated histopathology evaluation using deep learning

开发低成本、开源、本地制造的工作站和计算流程,用于利用深度学习进行自动化组织病理学评估

Choudhury, Divya; Dolezal, James M; Dyer, Emma; Kochanny, Sara; Ramesh, Siddhi; Howard, Frederick M; Margalus, Jayson R; Schroeder, Amelia; Schulte, Jefree; Garassino, Marina C; Kather, Jakob N; Pearson, Alexander T