Results 11 to 20 of about 71,340 (293)

Spatial transcriptomics coming of age [PDF]

Nature Reviews Genetics, 2019
Two new spatial transcriptomics techniques published in Nature and Science bring us an important step closer to the goal of achieving transcriptome-wide data at single-cell resolution.
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Advances in spatial transcriptomics and its applications in cancer research

Molecular Cancer
Malignant tumors have increasing morbidity and high mortality, and their occurrence and development is a complicate process. The development of sequencing technologies enabled us to gain a better understanding of the underlying genetic and molecular ...
Yang Jin, Yuanli Zuo, Gang Li, Wenrong Liu, Yitong Pan, Ting Fan, Xin Fu, Xiaojun Yao, Yong Peng   +8 more
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Computational solutions for spatial transcriptomics

Computational and Structural Biotechnology Journal, 2022
Transcriptome level expression data connected to the spatial organization of the cells and molecules would allow a comprehensive understanding of how gene expression is connected to the structure and function in the biological systems. The spatial transcriptomics platforms may soon provide such information.
Iivari Kleino, Paulina Frolovaitė, Tomi Suomi, Laura L. Elo   +3 more
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Opportunities and challenges in the application of single-cell and spatial transcriptomics in plants

Frontiers in Plant Science, 2023
Single-cell and spatial transcriptomics have diverted researchers’ attention from the multicellular level to the single-cell level and spatial information.
Ce Chen, Yining Ge, Lingli Lu, Lingli Lu
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Bionanomedicine: A “Panacea” In Medicine? [PDF]

, 2017
Recent advances in nanotechnology, biotechnology, bioinformatics, and materials science have prompted novel developments in the field of nanomedicine. Enhancements in the theranostics, computational information, and management of diseases/disorders are ...
Anurogo, D. (Dito), Ikrar, T. (Taruna), Parikesit, A. A. (Arli)   +2 more
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Computational Cancer Biology: An Evolutionary Perspective [PDF]

, 2016
ISSN:1553-734XISSN:1553 ...
A Deutsch, A Fischer, A Gonzalez-Perez, A Roth, A Schuh, A Tofigh, A Trinh, AM Newman, BJ Raphael, C Guichard, C Tomasetti, C Tomasetti, CD Greenman, Chris D. Greenman, CO Nordling, CS Cooper, CS-O Attolini, E Purdom, E Szczurek, ER Fearon, F Michor, F Ronquist, F Strino, F Vandin, H Shahrabi Farahani, H Zare, I Bozic, I Bozic, I Martincorena, J Diaz, J Foo, J Rahnenführer, J Shendure, Jens Lagergren, JH Goldie, K Yuan, KI Kim, L Oesper, LJ Barber, LMF Merlo, LR Yates, M Consequences, M Gerlinger, M Gerstung, M Gerstung, M Gerstung, M Hjelm, M Kimmel, MDM Leiserson, MDM Leiserson, MDM Leiserson, MF Berger, MS Lawrence, N Beerenwinkel, N Beerenwinkel, N Beerenwinkel, N Crosetto, N Navin, NE Navin, Niko Beerenwinkel, P Armitage, P Eirew, P Haccou, PC Nowell, PS Larson, R Desper, R Durrett, RF Schwarz, Ruth Nussinov, S Constantinescu, S Nik-Zainal, S Nik-Zainal, S Turajlic, S Turajlic, SA Chowdhury, SA Chowdhury, SE Luria, T Sakoparnig, V Almendro, W Jiao, WJ Ewens, Y Iwasa   +81 more
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Spatially informed cell-type deconvolution for spatial transcriptomics

Nature Biotechnology, 2022
Many spatially resolved transcriptomic technologies do not have single-cell resolution but measure the average gene expression for each spot from a mixture of cells of potentially heterogeneous cell types. Here, we introduce a deconvolution method, conditional autoregressive-based deconvolution (CARD), that combines cell-type-specific expression ...
Ying Ma, Xiang Zhou
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Spatial transcriptomics

The American Journal of Pathology
Dataset of spatial transcriptomics of endometrium and ...
Pierre Isnard, Benjamin D. Humphreys
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Spatially informed clustering, integration, and deconvolution of spatial transcriptomics with GraphST

Nature Communications, 2023
Advances in spatial transcriptomics technologies have enabled the gene expression profiling of tissues while retaining spatial context. Here the authors present GraphST, a graph self-supervised contrastive learning method that learns informative and ...
Yahui Long, Kok Siong Ang, Mengwei Li, Kian Long Kelvin Chong, Raman Sethi, Chengwei Zhong, Hang Xu, Zhiwei Ong, Karishma Sachaphibulkij, Ao Chen, Li Zeng, Huazhu Fu, Min Wu, Lina Hsiu Kim Lim, Longqi Liu, Jinmiao Chen   +15 more
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Unraveling Heterogeneity in Epithelial Cell Fates of the Mammary Gland and Breast Cancer. [PDF]

, 2019
Fluidity in cell fate or heterogeneity in cell identity is an interesting cell biological phenomenon, which at the same time poses a significant obstacle for cancer therapy.
Doh, Hanna, Kessenbrock, Kai, Roose, Jeroen P, Samocha, Alexandr   +3 more
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