Literatura académica sobre el tema "Cellular deconvolution"
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Artículos de revistas sobre el tema "Cellular deconvolution"
Main, Martin J. y Andrew X. Zhang. "Advances in Cellular Target Engagement and Target Deconvolution". SLAS DISCOVERY: Advancing the Science of Drug Discovery 25, n.º 2 (20 de enero de 2020): 115–17. http://dx.doi.org/10.1177/2472555219897269.
Texto completoMenden, Kevin, Mohamed Marouf, Sergio Oller, Anupriya Dalmia, Daniel Sumner Magruder, Karin Kloiber, Peter Heutink y Stefan Bonn. "Deep learning–based cell composition analysis from tissue expression profiles". Science Advances 6, n.º 30 (julio de 2020): eaba2619. http://dx.doi.org/10.1126/sciadv.aba2619.
Texto completoSosina, Olukayode A., Matthew N. Tran, Kristen R. Maynard, Ran Tao, Margaret A. Taub, Keri Martinowich, Stephen A. Semick et al. "Strategies for cellular deconvolution in human brain RNA sequencing data". F1000Research 10 (4 de agosto de 2021): 750. http://dx.doi.org/10.12688/f1000research.50858.1.
Texto completoDiaz, Michael, Jasmine Tran, Nicole Natarelli, Akash Sureshkumar y 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, n.º 6 (13 de noviembre de 2023): 1170–73. http://dx.doi.org/10.25251/skin.7.6.15.
Texto completoKim, Boyoung. "DVDeconv: An Open-Source MATLAB Toolbox for Depth-Variant Asymmetric Deconvolution of Fluorescence Micrographs". Cells 10, n.º 2 (15 de febrero de 2021): 397. http://dx.doi.org/10.3390/cells10020397.
Texto completoTurner, J. N., B. Roysam, T. J. Holmes, D. H. Szarowski, W. Lin, S. Bhattacharyya, H. Ancin, R. Mackin y 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.
Texto completoAbbas, Alexander R., Kristen Wolslegel, Dhaya Seshasayee y 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.
Texto completoUdpa, L., V. M. Ayres, Yuan Fan, Qian Chen y S. A. Kumar. "Deconvolution of atomic force microscopy data for cellular and molecular imaging". IEEE Signal Processing Magazine 23, n.º 3 (mayo de 2006): 73–83. http://dx.doi.org/10.1109/msp.2006.1628880.
Texto completoBlum, Yuna, Marie-Claude Jaurand, Aurélien De Reyniès y Didier Jean. "Unraveling the cellular heterogeneity of malignant pleural mesothelioma through a deconvolution approach". Molecular & Cellular Oncology 6, n.º 4 (7 de mayo de 2019): 1610322. http://dx.doi.org/10.1080/23723556.2019.1610322.
Texto completoPoirier, Christopher C., Win Pin Ng, Douglas N. Robinson y Pablo A. Iglesias. "Deconvolution of the Cellular Force-Generating Subsystems that Govern Cytokinesis Furrow Ingression". PLoS Computational Biology 8, n.º 4 (26 de abril de 2012): e1002467. http://dx.doi.org/10.1371/journal.pcbi.1002467.
Texto completoTesis sobre el tema "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.
Texto completoTai, An-Shun y 戴安順. "Statistical Deconvolution Models for Inferring Cellular Heterogeneity". Thesis, 2019. http://ndltd.ncl.edu.tw/handle/grdcvb.
Texto completo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.
Texto completoLibros sobre el tema "Cellular deconvolution"
Clive, Standley, Hughes John y United States. National Aeronautics and Space Administration., eds. Iterative deconvolution of X-ray and optical SNR images. [Washington, DC: National Aeronautics and Space Administration, 1992.
Buscar texto completoCapítulos de libros sobre el tema "Cellular deconvolution"
Howell, Gareth y Kyle Dent. "Bioimaging: light and electron microscopy". En Tools and Techniques in Biomolecular Science. Oxford University Press, 2013. http://dx.doi.org/10.1093/hesc/9780199695560.003.0017.
Texto completoMarks II, Robert J. "Signal and Image Synthesis: Alternating Projections Onto Convex Sets". En Handbook of Fourier Analysis & Its Applications. Oxford University Press, 2009. http://dx.doi.org/10.1093/oso/9780195335927.003.0016.
Texto completoActas de conferencias sobre el tema "Cellular deconvolution"
Eisenberg, Marisa, Joshua Ash y Dan Siegal-Gaskins. "In silicosynchronization of cellular populations through expression data deconvolution". En the 48th Design Automation Conference. New York, New York, USA: ACM Press, 2011. http://dx.doi.org/10.1145/2024724.2024906.
Texto completoMir, Mustafa, S. Derin Babacan, Michael Bednarz, Minh N. Do, Ido Golding y Gabriel Popescu. "Imaging sub-cellular structures using three-dimensional sparse deconvolution SLIM". En Biomedical Optics. Washington, D.C.: OSA, 2012. http://dx.doi.org/10.1364/biomed.2012.bm4b.2.
Texto completoRathnayake, S., B. Ditz, J. Van Nijnatten, C. Brandsma, W. Timens, P. Hiemstra, N. Ten Hacken et al. "Influence of smoking on bronchial epithelial cell composition by cellular deconvolution and IHC". En ERS International Congress 2022 abstracts. European Respiratory Society, 2022. http://dx.doi.org/10.1183/13993003.congress-2022.666.
Texto completoChen, Li, peter Choyke, Robert Clarke, Zaver Bhujwalla y Yue Wang. "Abstract A10: Unsupervised deconvolution of dynamic imaging reveals intratumor vascular heterogeneity and repopulation dynamics". En 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.
Texto completoAdams, T., J. C. Schupp, J. E. McDonough, F. Ahangari, G. DeIuliis, X. Yan, I. O. Rosas y N. Kaminski. "Deconvolution of Bulk RNAseq Datasets Confirms Substantial Cellular Population Shifts in the Distal Lung in IPF". En 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.
Texto completoMiheecheva, Natalia, Maria Sorokina, Akshaya Ramachandran, Yang Lyu, Danil Stupichev, Alexander Bagaev, Ekaterina Postovalova et al. "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". En 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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