Relevant bibliographies by topics / Spatial transcriptomic

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Spatial transcriptomic'

Author: Grafiati
Published: 21 September 2024
Last updated: 26 July 2025

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Journal articles on the topic "Spatial transcriptomic"

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Li, Youcheng, Leann Lac, Qian Liu, and Pingzhao Hu. "ST-CellSeg: Cell segmentation for imaging-based spatial transcriptomics using multi-scale manifold learning." PLOS Computational Biology 20, no. 6 (2024): e1012254. http://dx.doi.org/10.1371/journal.pcbi.1012254.

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Spatial transcriptomics has gained popularity over the past decade due to its ability to evaluate transcriptome data while preserving spatial information. Cell segmentation is a crucial step in spatial transcriptomic analysis, as it enables the avoidance of unpredictable tissue disentanglement steps. Although high-quality cell segmentation algorithms can aid in the extraction of valuable data, traditional methods are frequently non-spatial, do not account for spatial information efficiently, and perform poorly when confronted with the problem of spatial transcriptome cell segmentation with var
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Lv, Zhuo, Shuaijun Jiang, Shuxin Kong, et al. "Advances in Single-Cell Transcriptome Sequencing and Spatial Transcriptome Sequencing in Plants." Plants 13, no. 12 (2024): 1679. http://dx.doi.org/10.3390/plants13121679.

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“Omics” typically involves exploration of the structure and function of the entire composition of a biological system at a specific level using high-throughput analytical methods to probe and analyze large amounts of data, including genomics, transcriptomics, proteomics, and metabolomics, among other types. Genomics characterizes and quantifies all genes of an organism collectively, studying their interrelationships and their impacts on the organism. However, conventional transcriptomic sequencing techniques target population cells, and their results only reflect the average expression levels
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Chen, Tsai-Ying, Li You, Jose Angelito U. Hardillo, and Miao-Ping Chien. "Spatial Transcriptomic Technologies." Cells 12, no. 16 (2023): 2042. http://dx.doi.org/10.3390/cells12162042.

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Spatial transcriptomic technologies enable measurement of expression levels of genes systematically throughout tissue space, deepening our understanding of cellular organizations and interactions within tissues as well as illuminating biological insights in neuroscience, developmental biology and a range of diseases, including cancer. A variety of spatial technologies have been developed and/or commercialized, differing in spatial resolution, sensitivity, multiplexing capability, throughput and coverage. In this paper, we review key enabling spatial transcriptomic technologies and their applic
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Gorbunova, Vera. "COMPARATIVE TRANSCRIPTOMIC OF LONGEVITY." Innovation in Aging 7, Supplement_1 (2023): 432. http://dx.doi.org/10.1093/geroni/igad104.1423.

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Abstract Transcriptome analysis provides a nuanced view into the changes that occur in cells and tissues. Transcriptome changes rapidly and reproducibly in response to physiological influences and environmental insults. Recent years have seen an exponential increase in transcriptome data at bulk, single cell and spatial resolution that allows insights into the mechanisms and regulatory pathways of aging and longevity. In this session Drs. Gorbunova (University of Rochester) and Gladyshev (Harvard Medical School) will discuss comparative transcriptomics of longevity across species with diverse
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Callaway, Edward M., Hong-Wei Dong, Joseph R. Ecker, et al. "A multimodal cell census and atlas of the mammalian primary motor cortex." Nature 598, no. 7879 (2021): 86–102. http://dx.doi.org/10.1038/s41586-021-03950-0.

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AbstractHere we report the generation of a multimodal cell census and atlas of the mammalian primary motor cortex as the initial product of the BRAIN Initiative Cell Census Network (BICCN). This was achieved by coordinated large-scale analyses of single-cell transcriptomes, chromatin accessibility, DNA methylomes, spatially resolved single-cell transcriptomes, morphological and electrophysiological properties and cellular resolution input–output mapping, integrated through cross-modal computational analysis. Our results advance the collective knowledge and understanding of brain cell-type orga
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Lee, Sumin, and Amos Lee. "Abstract 2079: Spatially guided single-cell analysis integrating spatial transcriptomics and spatial cell sorting for in-depth profiling." Cancer Research 85, no. 8_Supplement_1 (2025): 2079. https://doi.org/10.1158/1538-7445.am2025-2079.

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Spatial transcriptomics technologies, such as Xenium, provide detailed spatial mapping of gene expression at the single-cell level, revealing intricate tissue organization and distinct cell populations. However, translating these spatial insights into actionable molecular information often requires further downstream analysis of specific cells of interest. To address this, we introduce an integrated workflow combining SLACS (Spatially-resolved Laser-Activated Cell Sorting) with Xenium-derived spatial data, enabling targeted isolation and in-depth transcriptomic analysis of defined cell populat
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Danaher, Patrick, Michael Patrick, Shanshan He, et al. "Abstract 751: High-resolution and AI-enabled single-cell spatial transcriptomics and histopathology integrated to reveal tumor differentiation and immune exclusion in skin squamous cell carcinoma." Cancer Research 85, no. 8_Supplement_1 (2025): 751. https://doi.org/10.1158/1538-7445.am2025-751.

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Abstract Skin squamous cell carcinoma (SCC) is characterized by heterogeneity in differentiation states and immune exclusion within the tumor microenvironment (TME). Using the Bruker Spatial Biology CosMx® Whole Transcriptome (WTX) panel, which profiles approximately 19, 000 genes at single-cell resolution, we examined spatial gene expression in FFPE SCC sections. Individual single cell boundaries were defined utilizing a trained AI cell segmentation model. H&E staining on the same tissue provided histopathological context, enabling the integration of molecular and morphological findings.
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Zhou, Jun, Shengxi Wang, Ming Liu, and Zhaopei Li. "Effect of cryoablation on the spatial transcriptomic landscape of the immune microenvironment in non-small cell lung cancer." Journal of Cancer Research and Therapeutics 20, no. 7 (2024): 2141–47. https://doi.org/10.4103/jcrt.jcrt_1887_24.

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ABSTRACT Background: Cryoablation induces antitumor immune responses. Spatial transcriptomic landscape technology has been used to determine the micron-level panoramic transcriptomics of tissue slices in situ. Methods: The effects of cryoablation on the immune microenvironment in non-small cell lung cancer (NSCLC) were explored by comparing the Whole Transcriptome Atlas (WTA) panel of immune cells before and after cryoablation using the spatial transcriptomic landscape. Results: The bioinformatics analysis showed that cryoablation significantly affected the WTA of immune cells, particularly ge
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He, Shanshan, Liang Zhang, Michael Patrick, et al. "Abstract 2068: Mapping the spatial whole transcriptome from normal to tumor tissue in renal clear cell carcinoma: tumorigenesis and microenvironmental shifts at single-cell resolution." Cancer Research 85, no. 8_Supplement_1 (2025): 2068. https://doi.org/10.1158/1538-7445.am2025-2068.

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Abstract Renal clear cell carcinoma (ccRCC) develops through significant molecular and spatial reprogramming, transforming normal kidney tissue into a malignant state and reshaping the tumor microenvironment. To investigate the transition from normal to cancerous tissue, we utilized the CosMx® Whole Transcriptome (WTX) panel to perform high-resolution spatial transcriptomic imaging of FFPE sections containing ccRCC and adjacent normal kidney. H&E staining on the same tissue sections enabled direct correlation of molecular profiles with histopathological features, uncovering novel insights
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Tu, Wenqian, and Lihua Zhang. "Integrating multiple spatial transcriptomics data using community-enhanced graph contrastive learning." PLOS Computational Biology 21, no. 4 (2025): e1012948. https://doi.org/10.1371/journal.pcbi.1012948.

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Due to the rapid development of spatial sequencing technologies, large amounts of spatial transcriptomic datasets have been generated across various technological platforms or different biological conditions (e.g., control vs. treatment). Spatial transcriptomics data coming from different platforms usually has different resolutions. Moreover, current methods do not consider the heterogeneity of spatial structures within and across slices when modeling spatial transcriptomics data with graph-based methods. In this study, we propose a community-enhanced graph contrastive learning-based method na
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Dissertations / Theses on the topic "Spatial transcriptomic"

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Castorena, Rodriguez Jimena. "Application of a spatial transcriptomic approach to assess medullar endothelial cells in vascular niches, chronic inflammation and fibrosis in myeloproliferative neoplasm patients." Electronic Thesis or Diss., université Paris-Saclay, 2025. http://www.theses.fr/2025UPASL022.

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Les néoplasmes myéloprolifératifs (NMP) sont des maladies rares du sang qui perturbent la production des cellules sanguines, augmentent le risque de complications vasculaires, dont l’evolution peut être une fibrose de la moelle osseuse (MO) dans un contexte inflammatoire. Notre étude porte sur les cellules endothéliales de la MO (CEMO) et leur implication dans ces maladies. Nous avons utilisé, pour la première fois sur des biopsies osseuses de patients, une approche de biologie spatiale innovante pour étudier les CEMO dans leur contexte architectural. Nous avons mis en évidence que les CEMO su
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Larsson, Ludvig. "Optimization of UMI counting strategies for Spatial Transcriptomics." Thesis, KTH, Skolan för kemi, bioteknologi och hälsa (CBH), 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-233838.

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Spatial Transcriptomics (ST) is a microarray-based RNA sequencing technology that allows for genome-wide transcriptome profiling of tissue sections with spatialresolution, which was published in Science by Ståhl and Salmén et al in 2016. Polyadenylated transcripts are captured on a microarray surface through hybridizationwith a barcoded DNA oligo that carries the information necessary to infer spatialposition and transcript uniqueness from the output sequencing reads. The ST protocolutilizes Unique Molecular Identifiers (UMIs) to remove PCR duplicates and obtain reliable estimates of gene coun
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Van, Leen Eric. "On the morphogenesis of the D. melanogaster pupa : a study on gene patterning and tissue folding." Electronic Thesis or Diss., Sorbonne université, 2020. http://www.theses.fr/2020SORUS387.

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Au cours du développement, la coordination des comportements cellulaires est essentielle à la formation d’organes complexes et fonctionnels. L’analyse de ces processus cellulaire est essentielle pour comprendre comment les tissues se forment au cours du développement. Pour ce faire, il est tout d’abord primordial d’identifier les gènes dont l’expression est corrélée avec chacun de ces processus cellulaires. Avec pour modèle la formation de l’épithélium dorsal (le notum) de la pupe de drosophile, mon travail de thèse a visé à identifier les mécanismes moléculaires qui gouvernent la régulation s
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Peiffer, Jason, Shail Kaushik, Hajime Sakai, et al. "A spatial dissection of the Arabidopsis floral transcriptome by MPSS." BioMed Central, 2008. http://hdl.handle.net/10150/610079.

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BACKGROUND:We have further characterized floral organ-localized gene expression in the inflorescence of Arabidopsis thaliana by comparison of massively parallel signature sequencing (MPSS) data. Six libraries of RNA sequence tags from immature inflorescence tissues were constructed and matched to their respective loci in the annotated Arabidopsis genome. These signature libraries survey the floral transcriptome of wild-type tissue as well as the floral homeotic mutants, apetala1, apetala3, agamous, a superman/apetala1 double mutant, and differentiated ovules dissected from the gynoecia of wild
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Jestin, Martin. "Modifications du microenvironnement stromal après irradiation localisée du côlon : identification de voies moléculaires pour optimiser le processus de régénération épithéliale." Electronic Thesis or Diss., Sorbonne université, 2024. https://accesdistant.sorbonne-universite.fr/login?url=https://theses-intra.sorbonne-universite.fr/2024SORUS165.pdf.

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Les cancers pelviens ont une prévalence élevée et sont principalement traités par radiothérapie. Si elle permet un contrôle de la tumeur, la radiothérapie peut également provoquer des lésions aux tissus sains environnants, entrainant des complications invalidantes définies comme une maladie à part entière, la «Pelvic Radiation Disease» ou PRD. Aujourd'hui, il n'existe pas de traitement curatif pour cette pathologie fibrosante. Ce projet vise à étudier le microenvironnement du côlon après irradiation afin d'identifier, à terme, des cibles thérapeutiques pour la prise en charge des séquelles col
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Currás, alonso Sandra. "Lung responses to radiation injury at the single cell level." Electronic Thesis or Diss., Université Paris sciences et lettres, 2021. http://www.theses.fr/2021UPSLS060.

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La radiothérapie constitue une option thérapeutique majeure pour le traitement du cancer du poumon. Néanmoins, la radiothérapie induit chez environ 5 à 20 % des patients traités des toxicités pulmonaires irréversibles précoces et tardives, telles que la pneumonite aiguë ou la fibrose pulmonaire induite par la radiothérapie (FPRI). La FPRI est caractérisée par une destruction progressive et irréversible de l'architecture alvéolaire avec perturbation des échanges gazeux conduisant à la mort des patients. Bien que l'ordre des événements moléculaires et cellulaires dans la progression vers la FPRI
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Lötstedt, Britta. "Towards spatial host-microbiome profiling." Licentiate thesis, KTH, Genteknologi, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-289384.

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Sequencing technologies and applications have pushed the limits and enabled novel studies of biological mechanisms, evolutionary relationships and communication networks between cells. The technical developments leading to single cell RNA-sequencing have enabled detection of rare cell populations while spatial resolution added insights into larger biological environments, like tissues and organs. Massively parallel sequencing has paved the way for integrated high-throughput analyses including that of studying gene expression, protein expression and mapping of microbial communities. This thesis
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Kaewsapsak, Pornchai. "Spatially-resolved transcriptomic mapping in live cells using peroxidase-mediated proximity biotinylation." Thesis, Massachusetts Institute of Technology, 2017. http://hdl.handle.net/1721.1/113972.

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Thesis: Ph. D., Massachusetts Institute of Technology, Department of Chemistry, 2017.<br>Cataloged from PDF version of thesis.<br>Includes bibliographical references.<br>The spatial organization of RNA within cells is crucial for the regulation of a wide range of biological functions, spanning all kingdoms of life. However, a general understanding of RNA localization has been hindered by a lack of simple, high-throughput methods for mapping the transcriptomes of subcellular compartments. Here, we developed two methods, termed APEX-RIP and APEX-Seq. APEX-RIP combines peroxidase-catalyzed, spati
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Mignardi, Marco. "In situ Sequencing : Methods for spatially-resolved transcriptome analysis." Doctoral thesis, Stockholms universitet, Institutionen för biokemi och biofysik, 2014. http://urn.kb.se/resolve?urn=urn:nbn:se:su:diva-110057.

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It is well known that cells in tissues display a large heterogeneity in gene expression due to differences in cell lineage origin and variation in the local environment at different sites in the tissue, a heterogeneity that is difficult to study by analyzing bulk RNA extracts from tissue. Recently, genome-wide transcriptome analysis technologies have enabled the analysis of this variation with single-cell resolution. In order to link the heterogeneity observed at molecular level with the morphological context of tissues, new methods are needed which achieve an additional level of information,
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Vickovic, Sanja. "Transcriptome-wide analysis in cells and tissues." Doctoral thesis, KTH, Genteknologi, 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-199447.

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High-throughput sequencing has greatly influenced the amount of data produced and biological questions asked and answered. Sequencing approaches have also enabled rapid development of related technological fields such as single-cell and spatially resolved expression profiling. The introductory parts of this thesis give an overview of the basic molecular and technological apparatus needed to analyse the transcriptome in cells and tissues. This is succeeded by a summary of present investigations that report recent advancements in RNA profiling. RNA integrity needs to be preserved for accurate ge
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Books on the topic "Spatial transcriptomic"

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Madan, Esha. Cutting Edge Artificial Intelligence, Spatial Transcriptomics and Proteomics Approaches to Analyze Cancer. Elsevier Science & Technology Books, 2024.

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Book chapters on the topic "Spatial transcriptomic"

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Uboh, Ndifereke, Sean Vargas, Victoria D. Diaz, and Brian P. Hermann. "Spatial Transcriptomic Analyses of Spermatogenesis." In Methods in Molecular Biology. Springer US, 2025. https://doi.org/10.1007/978-1-0716-4698-4_3.

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Lin, Yifan, Chenqi Wang, and Jinting Guan. "SpatialDSSC: Estimating Cell Type Abundance and Expression Profile from Spatial Transcriptomic Data." In Lecture Notes in Computer Science. Springer Nature Singapore, 2025. https://doi.org/10.1007/978-981-95-0030-7_5.

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Zhu, Qian. "A Hidden Markov Random Field Model for Detecting Domain Organizations from Spatial Transcriptomic Data." In Methods in Molecular Biology. Springer New York, 2019. http://dx.doi.org/10.1007/978-1-4939-9057-3_16.

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Nichterwitz, Susanne, Julio Aguila Benitez, Rein Hoogstraaten, Qiaolin Deng, and Eva Hedlund. "LCM-Seq: A Method for Spatial Transcriptomic Profiling Using Laser Capture Microdissection Coupled with PolyA-Based RNA Sequencing." In Methods in Molecular Biology. Springer New York, 2018. http://dx.doi.org/10.1007/978-1-4939-7213-5_6.

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Achim, Kaia, Hernando Martínez Vergara, and Jean-Baptiste Pettit. "Spatial Transcriptomics: Constructing a Single-Cell Resolution Transcriptome-Wide Expression Atlas." In Methods in Molecular Biology. Springer New York, 2018. http://dx.doi.org/10.1007/978-1-4939-7213-5_7.

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Raghubar, Arti M., Joanna Crawford, Kahli Jones, et al. "Spatial Transcriptomics in Kidney Tissue." In Methods in Molecular Biology. Springer US, 2023. http://dx.doi.org/10.1007/978-1-0716-3179-9_17.

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Sammeth, Michael, Susann Mudra, Sina Bialdiga, Beate Hartmannsberger, Sofia Kramer, and Heike Rittner. "Comparative Methods for Demystifying Spatial Transcriptomics." In Comparative Genomics. Springer US, 2024. http://dx.doi.org/10.1007/978-1-0716-3838-5_17.

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Haber, Ellie, Ajinkya Deshpande, Jian Ma, and Spencer Krieger. "Unified Integration of Spatial Transcriptomics Across Platforms." In Lecture Notes in Computer Science. Springer Nature Switzerland, 2025. https://doi.org/10.1007/978-3-031-90252-9_42.

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Charitakis, Natalie, Mirana Ramialison, and Hieu T. Nim. "Comparative Analysis of Packages and Algorithms for the Analysis of Spatially Resolved Transcriptomics Data." In Transcriptomics in Health and Disease. Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-87821-4_7.

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Ma, Cong, Uthsav Chitra, Shirley Zhang, and Benjamin J. Raphael. "Belayer: Modeling Discrete and Continuous Spatial Variation in Gene Expression from Spatially Resolved Transcriptomics." In Lecture Notes in Computer Science. Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-04749-7_33.

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Conference papers on the topic "Spatial transcriptomic"

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Li, Ning, Jiayidaer Badai, Dengjie Chen, Ming Xiao, and Le Zhang. "STARGATE: Spatial Transcriptomic Analysis with Recurrent and Graph Attention Techniques using Ensemble Learning." In 2024 IEEE International Conference on Bioinformatics and Biomedicine (BIBM). IEEE, 2024. https://doi.org/10.1109/bibm62325.2024.10822280.

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Li, Bingjun, Mostafa Karami, Masum Shah Junayed, and Sheida Nabavi. "Multi-modal Spatial Clustering for Spatial Transcriptomics Utilizing High-resolution Histology Images." In 2024 IEEE International Conference on Bioinformatics and Biomedicine (BIBM). IEEE, 2024. https://doi.org/10.1109/bibm62325.2024.10822051.

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Gao, Yanru, Feng Li, Fanhao Meng, Daohui Ge, Qianqian Ren, and Junliang Shang. "Spatial domains identification based on multi-view contrastive learning in spatial transcriptomics." In 2024 IEEE International Conference on Bioinformatics and Biomedicine (BIBM). IEEE, 2024. https://doi.org/10.1109/bibm62325.2024.10821770.

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Sun, Xingzhi, Charles Xu, João F. Rocha, et al. "Hyperedge Representations with Hypergraph Wavelets: Applications to Spatial Transcriptomics." In ICASSP 2025 - 2025 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2025. https://doi.org/10.1109/icassp49660.2025.10890269.

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Shi, Zhiceng, Shuailin Xue, Fangfang Zhu, and Wenwen Min. "High-Resolution Spatial Transcriptomics from Histology Images using HisToSGE." In 2024 IEEE International Conference on Bioinformatics and Biomedicine (BIBM). IEEE, 2024. https://doi.org/10.1109/bibm62325.2024.10822048.

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Fang, Donghai, Fangfang Zhu, and Wenwen Min. "Multi-Slice Spatial Transcriptomics Data Integration Analysis with STG3Net." In 2024 IEEE International Conference on Bioinformatics and Biomedicine (BIBM). IEEE, 2024. https://doi.org/10.1109/bibm62325.2024.10822331.

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Qu, Mingcheng, Yuncong Wu, Donglin Di, et al. "Boundary-Guided Learning for Gene Expression Prediction in Spatial Transcriptomics." In 2024 IEEE International Conference on Bioinformatics and Biomedicine (BIBM). IEEE, 2024. https://doi.org/10.1109/bibm62325.2024.10822232.

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Li, Xiaoyu, Wenwen Min, Shunfang Wang, Changmiao Wang, and Taosheng Xu. "Masked Conditional Diffusion Model with GNN for Spatial Transcriptomics Data Imputation." In 2024 IEEE International Conference on Bioinformatics and Biomedicine (BIBM). IEEE, 2024. https://doi.org/10.1109/bibm62325.2024.10822627.

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Huang, Lin, Xiaofei Liu, Shunfang Wang, and Wenwen Min. "Masked adversarial neural network for cell type deconvolution in spatial transcriptomics." In 2024 IEEE International Conference on Bioinformatics and Biomedicine (BIBM). IEEE, 2024. https://doi.org/10.1109/bibm62325.2024.10821782.

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Nishimura, Kazuya, Ryoma Bise, and Yasuhiro Kojima. "Towards Spatial Transcriptomics-Guided Pathological Image Recognition with Batch-Agnostic Encoder." In 2025 IEEE 22nd International Symposium on Biomedical Imaging (ISBI). IEEE, 2025. https://doi.org/10.1109/isbi60581.2025.10980754.

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Reports on the topic "Spatial transcriptomic"

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Brown Horowitz, Sigal, Eric L. Davis, and Axel Elling. Dissecting interactions between root-knot nematode effectors and lipid signaling involved in plant defense. United States Department of Agriculture, 2014. http://dx.doi.org/10.32747/2014.7598167.bard.

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Root-knot nematodes, Meloidogynespp., are extremely destructive pathogens with a cosmopolitan distribution and a host range that affects most crops. Safety and environmental concerns related to the toxicity of nematicides along with a lack of natural resistance sources threaten most crops in Israel and the U.S. This emphasizes the need to identify genes and signal mechanisms that could provide novel nematode control tactics and resistance breeding targets. The sedentary root-knot nematode (RKN) Meloidogynespp. secrete effectors in a spatial and temporal manner to interfere with and mimic multi
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