From Cellular Characteristics to Disease Diagnosis: Uncovering Phenotypes with Supercells
暂无分享,去创建一个
Wolfgang Losert | Lai Wei | Amos Maritan | Julián Candia | Meghan K. Driscoll | Angélique Biancotto | Ryan Maunu | Pradeep Dagur | J. Philip McCoy | H. Nida Sen | Kan Cao | Robert B. Nussenblatt | Jayanth R. Banavar | A. Maritan | J. Banavar | R. Maunu | W. Losert | R. Nussenblatt | P. Dagur | Lai Wei | A. Biancotto | M. Driscoll | J. Candia | H. Sen | K. Cao | J. P. Mccoy
[1] K. Sachs,et al. Causal Protein-Signaling Networks Derived from Multiparameter Single-Cell Data , 2005, Science.
[2] Gourab Mukherjee,et al. Innate immune response to homologous rotavirus infection in the small intestinal villous epithelium at single-cell resolution , 2012, Proceedings of the National Academy of Sciences.
[3] D. Grasso,et al. Flow cytometry. , 1998, Methods in molecular medicine.
[4] Neal S. Holter,et al. Dynamic modeling of gene expression data. , 2001, Proceedings of the National Academy of Sciences of the United States of America.
[5] Iftekhar Naim,et al. Swift: Scalable weighted iterative sampling for flow cytometry clustering , 2010, 2010 IEEE International Conference on Acoustics, Speech and Signal Processing.
[6] Xu-yan Yang,et al. Th22, but not Th17 Might be a Good Index to Predict the Tissue Involvement of Systemic Lupus Erythematosus , 2013, Journal of Clinical Immunology.
[7] Robert P. Lucht,et al. Publisher’s Note: Label-Free Bond-Selective Imaging by Listening to Vibrationally Excited Molecules [Phys. Rev. Lett. 106 , 238106 (2011)] , 2011 .
[8] Patrick S. Stumpf,et al. Nanog-dependent feedback loops regulate murine embryonic stem cell heterogeneity , 2012, Nature Cell Biology.
[9] Catalin C. Barbacioru,et al. mRNA-Seq whole-transcriptome analysis of a single cell , 2009, Nature Methods.
[10] M. Chavance. [Jackknife and bootstrap]. , 1992, Revue d'epidemiologie et de sante publique.
[11] Francis S. Collins,et al. A lamin A protein isoform overexpressed in Hutchinson–Gilford progeria syndrome interferes with mitosis in progeria and normal cells , 2007, Proceedings of the National Academy of Sciences.
[12] Hannah H. Chang,et al. Transcriptome-wide noise controls lineage choice in mammalian progenitor cells , 2008, Nature.
[13] David F. Keren,et al. Flow Cytometry in Clinical Diagnosis , 1994 .
[14] Lisa A Maier,et al. CD27 Expression on CD4+ T Cells Differentiates Effector from Regulatory T Cell Subsets in the Lung1 , 2009, The Journal of Immunology.
[15] Francis S. Collins,et al. Human laminopathies: nuclei gone genetically awry , 2006, Nature Reviews Genetics.
[16] Joachim Kohn,et al. Cytoskeleton-based forecasting of stem cell lineage fates , 2009, Proceedings of the National Academy of Sciences.
[17] R. Scheuermann,et al. Elucidation of seventeen human peripheral blood B‐cell subsets and quantification of the tetanus response using a density‐based method for the automated identification of cell populations in multidimensional flow cytometry data , 2010, Cytometry. Part B, Clinical cytometry.
[18] Ian H. Witten,et al. Chapter 1 – What's It All About? , 2011 .
[19] Wolfgang Losert,et al. Automated image analysis of nuclear shape: What can we learn from a prematurely aged cell? , 2012, Aging.
[20] Colin McCann,et al. LTB4 is a signal-relay molecule during neutrophil chemotaxis. , 2012, Developmental cell.
[21] N. Aghaeepour,et al. Automated analysis of multidimensional flow cytometry data improves diagnostic accuracy between mantle cell lymphoma and small lymphocytic lymphoma. , 2012, American journal of clinical pathology.
[22] Robert Gentleman,et al. flowCore: a Bioconductor package for high throughput flow cytometry , 2009, BMC Bioinformatics.
[23] Ian Witten,et al. Data Mining , 2000 .
[24] T. Nardò,et al. Circulating CD4+ CD25brightFOXP3+ regulatory T-cells are significantly reduced in bullous pemphigoid patients , 2012, Archives of Dermatological Research.
[25] Greg Finak,et al. Critical assessment of automated flow cytometry data analysis techniques , 2013, Nature Methods.
[26] H. Hoos,et al. RchyOptimyx: Cellular hierarchy optimization for flow cytometry , 2012, Cytometry. Part A : the journal of the International Society for Analytical Cytology.
[27] Nello Cristianini,et al. An Introduction to Support Vector Machines and Other Kernel-based Learning Methods , 2000 .
[28] Ian H. Witten,et al. Data Mining: Practical Machine Learning Tools and Techniques, 3/E , 2014 .
[29] Christian M Reidys,et al. Central and local limit theorems for RNA structures. , 2007, Journal of theoretical biology.
[30] P. Qiu. Inferring Phenotypic Properties from Single-Cell Characteristics , 2012, PloS one.
[31] Lai Wei,et al. MCAM-expressing CD4(+) T cells in peripheral blood secrete IL-17A and are significantly elevated in inflammatory autoimmune diseases. , 2011, Journal of autoimmunity.
[32] Pratip K. Chattopadhyay,et al. Early immunologic correlates of HIV protection can be identified from computational analysis of complex multivariate T-cell flow cytometry assays , 2012, Bioinform..
[33] Neal S. Holter,et al. Fundamental patterns underlying gene expression profiles: simplicity from complexity. , 2000, Proceedings of the National Academy of Sciences of the United States of America.
[34] Francis S Collins,et al. Rapamycin Reverses Cellular Phenotypes and Enhances Mutant Protein Clearance in Hutchinson-Gilford Progeria Syndrome Cells , 2011, Science Translational Medicine.
[35] Lihong V. Wang,et al. Label-free bond-selective imaging by listening to vibrationally excited molecules. , 2011, Physical review letters.
[36] S. Sealfon,et al. flowPeaks: a fast unsupervised clustering for flow cytometry data via K-means and density peak finding , 2012, Bioinform..
[37] I. Check,et al. Flow Cytometry in Clinical Diagnosis , 1990 .
[38] Gerry Leversha,et al. Foundations of modern probability (2nd edn), by Olav Kallenberg. Pp. 638. £49 (hbk). 2002. ISBN 0 387 95313 2 (Springer-Verlag). , 2004, The Mathematical Gazette.
[39] J. Rice. Mathematical Statistics and Data Analysis , 1988 .
[40] Ian H. Witten,et al. Data mining: practical machine learning tools and techniques, 3rd Edition , 1999 .
[41] O. Kallenberg. Foundations of Modern Probability , 2021, Probability Theory and Stochastic Modelling.
[42] Sean C. Bendall,et al. Single-Cell Mass Cytometry of Differential Immune and Drug Responses Across a Human Hematopoietic Continuum , 2011, Science.
[43] Karen N Conneely,et al. Inhibiting farnesylation of progerin prevents the characteristic nuclear blebbing of Hutchinson-Gilford progeria syndrome. , 2005, Proceedings of the National Academy of Sciences of the United States of America.
[44] Nir Hacohen,et al. Flow Cytometry, Amped Up , 2011, Science.
[45] Lani F. Wu,et al. Cellular Heterogeneity: Do Differences Make a Difference? , 2010, Cell.
[46] Nathalie Arbour,et al. Central nervous system recruitment of effector memory CD8+ T lymphocytes during neuroinflammation is dependent on α4 integrin , 2011, Brain : a journal of neurology.