A benchmark for evaluation of algorithms for identification of cellular correlates of clinical outcomes-Reference-Cited by-同舟云学术

A benchmark for evaluation of algorithms for identification of cellular correlates of clinical outcomes

Published:2015-10-08 Issue:1 Volume:89 Page:16-21
ISSN:1552-4922
Container-title:Cytometry Part A
language:en
Short-container-title:Cytometry

Author:

Aghaeepour Nima¹²³,Chattopadhyay Pratip⁴,Chikina Maria⁵,Dhaene Tom⁶,Van Gassen Sofie⁶⁷⁸,Kursa Miron⁹,Lambrecht Bart N.⁷⁸,Malek Mehrnoush¹,McLachlan G. J.¹⁰,Qian Yu¹¹,Qiu Peng¹²,Saeys Yvan⁷⁸,Stanton Rick¹¹,Tong Dong¹³¹⁴,Vens Celine⁷⁸¹⁵,Walkowiak Sławomir⁹,Wang Kui¹⁶¹⁷,Finak Greg¹⁸,Gottardo Raphael¹⁸,Mosmann Tim¹⁹,Nolan Garry P.³,Scheuermann Richard H.¹¹²⁰,Brinkman Ryan R.¹²

Affiliation:

1. Terry Fox Laboratory, British Columbia Cancer Agency; Vancouver British Columbia Canada

2. Department of Medical Genetics; University of British Columbia; Vancouver British Columbia Canada

3. Baxter Laboratory in Stem Cell Biology; Stanford University; Stanford California

4. ImmunoTechnology Section, Vaccine Research Center; National Institutes of Health; Washington DC

5. Department Computational and Systems Biology; University of Pittsburgh; Pittsburg Pennsylvania

6. Department of Information Technology; Ghent University-iMinds; Ghent Belgium

7. Inflammation Research Center, VIB; Ghent Belgium

8. Department of Respiratory Medicine; Ghent University Hospital; Ghent Belgium

9. Interdisciplinary Centre for Mathematical and Computational Modelling; University of Warsaw; Warsaw Poland

10. Department of Statistics; University of Queensland; St. Lucia Brisbane Australia 4072

11. J. Craig Venter Institute; La Jolla California

12. Department of Biomedical Engineering; Georgia Institute of Technology and Emory University; Atlanta Georgia

13. The John Van Geest Cancer Research Centre, Nottingham Trent University; Nottingham United Kingdom

14. Centre for Genomic and Experimental Medicine, Institute of Genetics and Molecular Medicine, University of Edinburgh; Edinburgh United Kingdom

15. Department of Public Health and Primary Care; kU Leuven Kulak; Kortrijk Belgium

16. Department of Mathematics; University of Queensland; St. Lucia Brisbane Australia

17. School of Medicine, Shihezi University; Xinjiang 832000 China

18. Vaccine and Infectious Disease Division; Fred Hutchinson Cancer Research Center, Seattle; Washington DC

19. David H. Smith Center for Vaccine Biology and Immunology; University of Rochester Medical Center; Rochester New York

20. Department of Pathology; University of California; San Diego California

Publisher

Wiley

Subject

Cell Biology,Histology,Pathology and Forensic Medicine

Link

http://onlinelibrary.wiley.com/wol1/doi/10.1002/cyto.a.22732/fullpdf

Reference40 articles.

1. Flow cytometry bioinformatics;O'Neill;PLoS Comput. Biol,2013

2. Critical assessment of automated flow cytometry data analysis techniques;Aghaeepour;Nat Methods,2013

3. Increasing age at HIV seroconversion from 18 to 40 years is associated with favorable virologic and immunologic responses to HAART;Weintrob;JAIDS J Acquired Immune Deficiency Syndromes,2008

4. Immunologic and virologic events in early HIV infection predict subsequent rate of progression;Ganesan;J Infect Dis,2010

5. MacQueen J. 281 297

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