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In recent years, the advent of spatial transcriptomics (ST) technology has unlocked unprecedented opportunities for delving into the complexities of gene expression patterns within intricate biological systems.
Identification of dysregulated genes in cutaneous squamous cell carcinoma
Chantip Dang, Marc Gottschling, Kizzie Manning, Eoin O’Currain, Sylke Schneider, Wolfram Sterry, Eggert Stockfleth, and Ingo Nindl · 2006
Earlier work this paper cites.
High stearoyl-coa desaturase 1 expression is associated with shorter survival in breast cancer patients
Ashley M Holder, Ana M Gonzalez-Angulo, et al · 2013
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Spatially resolved transcriptomics and beyond
Nicola Crosetto, Magda Bienko, and Alexander Van Oudenaarden · 2015
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Deep residual learning for image recognition
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2016
Earlier work this paper cites.
Spatial transcriptomics: paving the way for tissue-level systems biology
Andreas E Moor and Shalev Itzkovitz · 2017
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Densely connected convolutional networks
Gao Huang, Zhuang Liu, Laurens Van Der Maaten, and Kilian Q Weinberger · 2017
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Fatty acid synthase (fasn) as a therapeutic target in breast cancer
Javier A Menendez and Ruth Lupu · 2017
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Representation learning on graphs with jumping knowledge networks
Keyulu Xu, Chengtao Li, Yonglong Tian, Tomohiro Sonobe, Ken-ichi Kawarabayashi, and Stefanie Jegelka · 2018
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Systematic identification of the key candidate genes in breast cancer stroma
Yanxia Wang, Hui Xu, Baoan Zhu, Zhenling Qiu, and Zaijun Lin · 2018
Earlier work this paper cites.
Identification of biomarker for cutaneous squamous cell carcinoma using microarray data analysis
Wei Wei, Yan Chen, Jie Xu, Yu Zhou, Xinping Bai, Ming Yang, and Ju Zhu · 2018
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Elevated expression of gnas promotes breast cancer cell proliferation and migration via the pi3k/akt/snail1/e-cadherin axis
X Jin, L Zhu, Z Cui, J Tang, M Xie, and G Ren · 2019
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Expression of mhc class i, hla-a and hla-b identifies immune-activated breast tumors with favorable outcome
María Del Mar Noblejas-López et al · 2019
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From whole-mount to single-cell spatial assessment of gene expression in 3d
Lisa N Waylen, Hieu T Nim, Luciano G Martelotto, and Mirana Ramialison · 2020
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A deep learning model to predict rna-seq expression of tumours from whole slide images
Benoît Schmauch, Alberto Romagnoni, Elodie Pronier, Charlie Saillard, Pascale Maillé, Julien Calderaro, Aurélie Kamoun, Meriem Sefta, Sylvain Toldo, Mikhail Zaslavskiy, et al · 2020
Earlier work this paper cites.
Pathomic fusion: an integrated framework for fusing histopathology and genomic features for cancer diagnosis and prognosis
Richard J Chen, Ming Y Lu, Jingwen Wang, et al · 2020
Cited alongside, same era.
Integrating spatial gene expression and breast tumour morphology via deep learning
Bryan He, Ludvig Bergenstråhle, Linnea Stenbeck, Abubakar Abid, Alma Andersson, Åke Borg, Jonas Maaskola, Joakim Lundeberg, and James Zou · 2020
Cited alongside, same era.
A simple framework for contrastive learning of visual representations
Ting Chen, Simon Kornblith, Mohammad Norouzi, and Geoffrey Hinton · 2020
Cited alongside, same era.
Exploring tissue architecture using spatial transcriptomics
Anjali Rao, Dalia Barkley, Gustavo S França, and Itai Yanai · 2021
Cited alongside, same era.
Expansion sequencing: Spatially precise in situ transcriptomics in intact biological systems
Shahar Alon, Daniel R Goodwin, Anubhav Sinha, Asmamaw T Wassie, Fei Chen, Evan R Daugharthy, Yosuke Bando, Atsushi Kajita, Andrew G Xue, Karl Marrett, et al · 2021
Cited alongside, same era.
Deciphering spatial domains from spatially resolved transcriptomics with an adaptive graph attention auto-encoder
Kangning Dong and Shihua Zhang · 2022
Later among the works it cites.
Cell clustering for spatial transcriptomics data with graph neural networks
Jiachen Li, Siheng Chen, Xiaoyong Pan, Ye Yuan, and Hong-Bin Shen · 2022
Later among the works it cites.
Artificial intelligence in histopathology: enhancing cancer research and clinical oncology
Shmatko A, Ghaffari Laleh N, Gerstung M, and Kather JN · 2022
Later among the works it cites.
Cell segmentation in imaging-based spatial transcriptomics
Viktor Petukhov, Rosalind J Xu, Ruslan A Soldatov, Paolo Cadinu, Konstantin Khodosevich, Jeffrey R Moffitt, and Peter V Kharchenko · 2022
Later among the works it cites.
Spatial transcriptomics prediction from histology jointly through transformer and graph neural networks
Yuansong Zeng, Zhuoyi Wei, Weijiang Yu, Rui Yin, Yuchen Yuan, Bingling Li, Zhonghui Tang, Yutong Lu, and Yuedong Yang · 2022
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Integrating single-cell and spatial transcriptomics to elucidate intercellular tissue dynamics
Sophia K Longo, Margaret G Guo, Andrew L Ji, and Paul A Khavari · 2021
Cited alongside, same era.
Spatial transcriptomics at subspot resolution with bayesspace
Edward Zhao, Matthew R Stone, Xing Ren, et al · 2021
Cited alongside, same era.
Leveraging information in spatial transcriptomics to predict super-resolution gene expression from histology images in tumors
Minxing Pang, Kenong Su, and Mingyao Li · 2021
Cited alongside, same era.
An image is worth 16x16 words: Transformers for image recognition at scale
Alexey Dosovitskiy, Lucas Beyer, Alexander Kolesnikov, Dirk Weissenborn, Xiaohua Zhai, Thomas Unterthiner, Mostafa Dehghani, Matthias Minderer, Georg Heigold, Sylvain Gelly, Jakob Uszkoreit, and Neil Houlsby · 2021
Cited alongside, same era.
Learning transferable visual models from natural language supervision
Alec Radford, Jong Wook Kim, Chris Hallacy, Aditya Ramesh, Gabriel Goh, Sandhini Agarwal, Girish Sastry, Amanda Askell, Pamela Mishkin, Jack Clark, et al · 2021
Cited alongside, same era.
Prognostic impact of immunoglobulin kappa c (igkc) in early breast cancer
Marcus Schmidt, Karolina Edlund, Jan G Hengstler, Anne-Sophie Heimes, Katrin Almstedt, Antje Lebrecht, Slavomir Krajnak, Marco J Battista, Walburgis Brenner, Annette Hasenburg, et al · 2021
Cited alongside, same era.
Expression of hla-dr in cytotoxic t lymphocytes: A validated predictive biomarker and a potential therapeutic strategy in breast cancer
Diana P Saraiva, Sofia Azeredo-Lopes, António, et al · 2021
Cited alongside, same era.
Deciphering tumor ecosystems at super resolution from spatial transcriptomics with tesla
Jian Hu, Kyle Coleman, Daiwei Zhang, Edward B Lee, Humam Kadara, Linghua Wang, and Mingyao Li · 2023
Later among the works it cites.
Unsupervised spatially embedded deep representation of spatial transcriptomics
Hang Xu, Huazhu Fu, Yahui Long, Kok Siong Ang, Raman Sethi, Kelvin Chong, Mengwei Li, Rom Uddamvathanak, Hong Kai Lee, Jingjing Ling, et al · 2024
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Dimensionality reduction and denoising of spatial transcriptomics data using dual-channel masked graph autoencoder
Wenwen Min, Donghai Fang, Jinyu Chen, and Shihua Zhang · 2024
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Inferring super-resolution tissue architecture by integrating spatial transcriptomics with histology
Daiwei Zhang, Amelia Schroeder, Hanying Yan, Haochen Yang, Jian Hu, Michelle YY Lee, Kyung S Cho, Katalin Susztak, George X Xu, Michael D Feldman, et al · 2024
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Xiaoyu Li, Wenwen Min, Shunfang Wang, Changmiao Wang, and Taosheng Xu · 2024
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Innovative super-resolution in spatial transcriptomics: a transformer model exploiting histology images and spatial gene expression
Chongyue Zhao, Zhongli Xu, Xinjun Wang, Shiyue Tao, William A MacDonald, Kun He, Amanda C Poholek, Kong Chen, Heng Huang, and Wei Chen · 2024
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THItoGene: a deep learning method for predicting spatial transcriptomics from histological images
Yuran Jia, Junliang Liu, Li Chen, Tianyi Zhao, and Yadong Wang · 2024
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Spatially resolved gene expression prediction from histology images via bi-modal contrastive learning
Ronald Xie, Kuan Pang, et al · 2024
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