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Amelia Schroeder

Showing results (1-10 of 20) with videos related to

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Nature Methods|August 9, 2024
Unlocking the power of spatial omics with AIKyle Coleman, Amelia Schroeder, Mingyao Li
Communications Biology|April 7, 2023
SpaDecon: cell-type deconvolution in spatial transcriptomics with semi-supervised learningKyle Coleman, Jian Hu, Amelia Schroeder, et al.
Computational and Structural Biotechnology Journal|July 21, 2021
Statistical and machine learning methods for spatially resolved transcriptomics with histologyJian Hu, Amelia Schroeder, Kyle Coleman, et al.
Nature Machine Intelligence|March 6, 2023
A multi-use deep learning method for CITE-seq and single-cell RNA-seq data integration with cell surface protein prediction and imputationJustin Lakkis, Amelia Schroeder, Kenong Su, et al.
Biorxiv : the Preprint Server for Biology|May 13, 2026
Pixel2Gene enables histology-guided reconstruction and prediction of spatial gene expressionSicong Yao, Amelia Schroeder, Shunzhou Jiang, et al.
Nature Communications|July 8, 2023
Leveraging spatial transcriptomics data to recover cell locations in single-cell RNA-seq with CeLEryQihuang Zhang, Shunzhou Jiang, Amelia Schroeder, et al.
Nature Methods|October 29, 2021
SpaGCN: Integrating gene expression, spatial location and histology to identify spatial domains and spatially variable genes by graph convolutional networkJian Hu, Xiangjie Li, Kyle Coleman, et al.
Scientific Reports|June 1, 2019
Global geographic patterns of heterospecific pollen receipt help uncover potential ecological and evolutionary impacts across plant communities worldwideGerardo Arceo-Gómez, Amelia Schroeder, Cristopher Albor, et al.
Ebiomedicine|August 28, 2024
Developing a low-cost, open-source, locally manufactured workstation and computational pipeline for automated histopathology evaluation using deep learningDivya Choudhury, James M Dolezal, Emma Dyer, et al.
Biorxiv : the Preprint Server for Biology|March 18, 2026
Multiscale confidence quantification for virtual spatial transcriptomics with UTOPIAKaitian Jin, Zihao Chen, Xiaokang Yu, et al.
Pageof 2

Showing results (1-10 of 20) with videos related to

Sort By:
Pageof 2
Nature Methods|August 9, 2024
Unlocking the power of spatial omics with AIKyle Coleman, Amelia Schroeder, Mingyao Li
Communications Biology|April 7, 2023
SpaDecon: cell-type deconvolution in spatial transcriptomics with semi-supervised learningKyle Coleman, Jian Hu, Amelia Schroeder, et al.
Computational and Structural Biotechnology Journal|July 21, 2021
Statistical and machine learning methods for spatially resolved transcriptomics with histologyJian Hu, Amelia Schroeder, Kyle Coleman, et al.
Nature Machine Intelligence|March 6, 2023
A multi-use deep learning method for CITE-seq and single-cell RNA-seq data integration with cell surface protein prediction and imputationJustin Lakkis, Amelia Schroeder, Kenong Su, et al.
Biorxiv : the Preprint Server for Biology|May 13, 2026
Pixel2Gene enables histology-guided reconstruction and prediction of spatial gene expressionSicong Yao, Amelia Schroeder, Shunzhou Jiang, et al.
Nature Communications|July 8, 2023
Leveraging spatial transcriptomics data to recover cell locations in single-cell RNA-seq with CeLEryQihuang Zhang, Shunzhou Jiang, Amelia Schroeder, et al.
Nature Methods|October 29, 2021
SpaGCN: Integrating gene expression, spatial location and histology to identify spatial domains and spatially variable genes by graph convolutional networkJian Hu, Xiangjie Li, Kyle Coleman, et al.
Scientific Reports|June 1, 2019
Global geographic patterns of heterospecific pollen receipt help uncover potential ecological and evolutionary impacts across plant communities worldwideGerardo Arceo-Gómez, Amelia Schroeder, Cristopher Albor, et al.
Ebiomedicine|August 28, 2024
Developing a low-cost, open-source, locally manufactured workstation and computational pipeline for automated histopathology evaluation using deep learningDivya Choudhury, James M Dolezal, Emma Dyer, et al.
Biorxiv : the Preprint Server for Biology|March 18, 2026
Multiscale confidence quantification for virtual spatial transcriptomics with UTOPIAKaitian Jin, Zihao Chen, Xiaokang Yu, et al.
Pageof 2