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Artykuły w czasopismach na temat "Cellular deconvolution"
Main, Martin J., i Andrew X. Zhang. "Advances in Cellular Target Engagement and Target Deconvolution". SLAS DISCOVERY: Advancing the Science of Drug Discovery 25, nr 2 (20.01.2020): 115–17. http://dx.doi.org/10.1177/2472555219897269.
Pełny tekst źródłaMenden, Kevin, Mohamed Marouf, Sergio Oller, Anupriya Dalmia, Daniel Sumner Magruder, Karin Kloiber, Peter Heutink i Stefan Bonn. "Deep learning–based cell composition analysis from tissue expression profiles". Science Advances 6, nr 30 (lipiec 2020): eaba2619. http://dx.doi.org/10.1126/sciadv.aba2619.
Pełny tekst źródłaSosina, Olukayode A., Matthew N. Tran, Kristen R. Maynard, Ran Tao, Margaret A. Taub, Keri Martinowich, Stephen A. Semick i in. "Strategies for cellular deconvolution in human brain RNA sequencing data". F1000Research 10 (4.08.2021): 750. http://dx.doi.org/10.12688/f1000research.50858.1.
Pełny tekst źródłaDiaz, Michael, Jasmine Tran, Nicole Natarelli, Akash Sureshkumar i Mahtab Forouzandeh. "Cellular Deconvolution Reveals Unique Findings in Several Cell Type Fractions Within the Basal Cell Carcinoma Tumor Microenvironment". SKIN The Journal of Cutaneous Medicine 7, nr 6 (13.11.2023): 1170–73. http://dx.doi.org/10.25251/skin.7.6.15.
Pełny tekst źródłaKim, Boyoung. "DVDeconv: An Open-Source MATLAB Toolbox for Depth-Variant Asymmetric Deconvolution of Fluorescence Micrographs". Cells 10, nr 2 (15.02.2021): 397. http://dx.doi.org/10.3390/cells10020397.
Pełny tekst źródłaTurner, J. N., B. Roysam, T. J. Holmes, D. H. Szarowski, W. Lin, S. Bhattacharyya, H. Ancin, R. Mackin i D. Becker. "Visualization and quantitation of cellular and tissue anatomy by 3D light microscopy". Proceedings, annual meeting, Electron Microscopy Society of America 52 (1994): 928–29. http://dx.doi.org/10.1017/s0424820100172371.
Pełny tekst źródłaAbbas, Alexander R., Kristen Wolslegel, Dhaya Seshasayee i Hilary F. Clark. "Deconvolution of Blood Microarray Data Elucidates Cellular Activation Patterns in SLE". Clinical Immunology 123 (2007): S125—S126. http://dx.doi.org/10.1016/j.clim.2007.03.536.
Pełny tekst źródłaUdpa, L., V. M. Ayres, Yuan Fan, Qian Chen i S. A. Kumar. "Deconvolution of atomic force microscopy data for cellular and molecular imaging". IEEE Signal Processing Magazine 23, nr 3 (maj 2006): 73–83. http://dx.doi.org/10.1109/msp.2006.1628880.
Pełny tekst źródłaBlum, Yuna, Marie-Claude Jaurand, Aurélien De Reyniès i Didier Jean. "Unraveling the cellular heterogeneity of malignant pleural mesothelioma through a deconvolution approach". Molecular & Cellular Oncology 6, nr 4 (7.05.2019): 1610322. http://dx.doi.org/10.1080/23723556.2019.1610322.
Pełny tekst źródłaPoirier, Christopher C., Win Pin Ng, Douglas N. Robinson i Pablo A. Iglesias. "Deconvolution of the Cellular Force-Generating Subsystems that Govern Cytokinesis Furrow Ingression". PLoS Computational Biology 8, nr 4 (26.04.2012): e1002467. http://dx.doi.org/10.1371/journal.pcbi.1002467.
Pełny tekst źródłaRozprawy doktorskie na temat "Cellular deconvolution"
Wang, Chuangqi. "Machine Learning Pipelines for Deconvolution of Cellular and Subcellular Heterogeneity from Cell Imaging". Digital WPI, 2019. https://digitalcommons.wpi.edu/etd-dissertations/587.
Pełny tekst źródłaTai, An-Shun, i 戴安順. "Statistical Deconvolution Models for Inferring Cellular Heterogeneity". Thesis, 2019. http://ndltd.ncl.edu.tw/handle/grdcvb.
Pełny tekst źródła國立清華大學
統計學研究所
107
Tumor tissue samples comprise a mixture of cancerous and surrounding normal cells. Inferring the cell heterogeneity of tumors is critical to the understanding of cancer prognosis and the treatment decisions. Compared with the experimental methods of using cell sorting technology to isolate cell components, in silico decomposition of mixed cell samples is faster and cheaper. The present study introduces three novel statistical approaches, CloneDeMix, BayICE, and PEACH, for different issues to perform the cellular proportion estimation as well as the genomic inference. First, CloneDeMix adopts clustering approach to dissect the tumor subclonal architecture induced by copy number aberration of genes through DNA sequencing data. Different from CloneDeMix analyzing cancerous cell populations, BayICE next estimates the components of tumor-infiltrating cells such as immune cells via a Bayesian framework with stochastic variable selection. Last, PEACH uses a penalized deconvolution model based on transcriptomic data to investigate the phenomenon that the genes of the particular cell types express inconsistently after cell sorting. These models were validated through simulated data and real data to demonstrate the performance of deconvolution. Furthermore, the analysis of cancer and immune-related diseases showed the results associated with biological interpretation. All of the works are implemented on the corresponding R packages which are publicly available to perform the deconvolution analysis.
Chuang, Tony Chih-Yuan. "The three-dimensional (3D) organization of telomeres during cellular transformation". 2010. http://hdl.handle.net/1993/4228.
Pełny tekst źródłaKsiążki na temat "Cellular deconvolution"
Clive, Standley, Hughes John i United States. National Aeronautics and Space Administration., red. Iterative deconvolution of X-ray and optical SNR images. [Washington, DC: National Aeronautics and Space Administration, 1992.
Znajdź pełny tekst źródłaCzęści książek na temat "Cellular deconvolution"
Howell, Gareth, i Kyle Dent. "Bioimaging: light and electron microscopy". W Tools and Techniques in Biomolecular Science. Oxford University Press, 2013. http://dx.doi.org/10.1093/hesc/9780199695560.003.0017.
Pełny tekst źródłaMarks II, Robert J. "Signal and Image Synthesis: Alternating Projections Onto Convex Sets". W Handbook of Fourier Analysis & Its Applications. Oxford University Press, 2009. http://dx.doi.org/10.1093/oso/9780195335927.003.0016.
Pełny tekst źródłaStreszczenia konferencji na temat "Cellular deconvolution"
Eisenberg, Marisa, Joshua Ash i Dan Siegal-Gaskins. "In silicosynchronization of cellular populations through expression data deconvolution". W the 48th Design Automation Conference. New York, New York, USA: ACM Press, 2011. http://dx.doi.org/10.1145/2024724.2024906.
Pełny tekst źródłaMir, Mustafa, S. Derin Babacan, Michael Bednarz, Minh N. Do, Ido Golding i Gabriel Popescu. "Imaging sub-cellular structures using three-dimensional sparse deconvolution SLIM". W Biomedical Optics. Washington, D.C.: OSA, 2012. http://dx.doi.org/10.1364/biomed.2012.bm4b.2.
Pełny tekst źródłaRathnayake, S., B. Ditz, J. Van Nijnatten, C. Brandsma, W. Timens, P. Hiemstra, N. Ten Hacken i in. "Influence of smoking on bronchial epithelial cell composition by cellular deconvolution and IHC". W ERS International Congress 2022 abstracts. European Respiratory Society, 2022. http://dx.doi.org/10.1183/13993003.congress-2022.666.
Pełny tekst źródłaChen, Li, peter Choyke, Robert Clarke, Zaver Bhujwalla i Yue Wang. "Abstract A10: Unsupervised deconvolution of dynamic imaging reveals intratumor vascular heterogeneity and repopulation dynamics". W Abstracts: AACR Special Conference on Cellular Heterogeneity in the Tumor Microenvironment; February 26 — March 1, 2014; San Diego, CA. American Association for Cancer Research, 2015. http://dx.doi.org/10.1158/1538-7445.chtme14-a10.
Pełny tekst źródłaAdams, T., J. C. Schupp, J. E. McDonough, F. Ahangari, G. DeIuliis, X. Yan, I. O. Rosas i N. Kaminski. "Deconvolution of Bulk RNAseq Datasets Confirms Substantial Cellular Population Shifts in the Distal Lung in IPF". W American Thoracic Society 2020 International Conference, May 15-20, 2020 - Philadelphia, PA. American Thoracic Society, 2020. http://dx.doi.org/10.1164/ajrccm-conference.2020.201.1_meetingabstracts.a2248.
Pełny tekst źródłaMiheecheva, Natalia, Maria Sorokina, Akshaya Ramachandran, Yang Lyu, Danil Stupichev, Alexander Bagaev, Ekaterina Postovalova i in. "Abstract 161: Evaluating the clinical utility of RNA-seq-based PD-L1 expression and cellular deconvolution as alternatives to conventional immunohistochemistry in clear cell renal cell carcinoma". W Proceedings: AACR Annual Meeting 2021; April 10-15, 2021 and May 17-21, 2021; Philadelphia, PA. American Association for Cancer Research, 2021. http://dx.doi.org/10.1158/1538-7445.am2021-161.
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