From Cellular Characteristics to Disease Diagnosis: Uncovering Phenotypes with Supercells

PLoS Computational Biology

Volumn 9, Issue 9, 2013, Pages

  Candia, Julián ^a,b,c   Maunu, Ryan ^a   Driscoll, Meghan ^a   Biancotto, Angélique ^d,e   Dagur, Pradeep ^e   McCoy, J Philip ^d,e   Sen, H Nida ^f   Wei, Lai ^f   Maritan, Amos ^g   Cao, Kan ^a   Nussenblatt, Robert B ^f   Banavar, Jayanth R ^a   Losert, Wolfgang ^a  

^a University of Maryland   (United States)

^b UNIVERSITY OF MARYLAND SCHOOL OF MEDICINE   (United States)

^c Instituto de Fisica de Liquidos y Sistemas Biologicos   (Argentina)

^d NATIONAL INSTITUTES OF HEALTH   (United States)

^e NATIONAL HEART LUNG AND BLOOD INSTITUTE   (United States)

^f NATIONAL EYE INSTITUTE   (United States)

^g UNIVERSITY OF PADOVA   (Italy)

Author keywords

[No Author keywords available]

Indexed keywords

CYTOLOGY; DIAGNOSIS; DISEASES; GENE EXPRESSION; LEARNING SYSTEMS; TUMORS;

'CURRENT; CELL BIOLOGY; CELLULARS; DISEASE DIAGNOSIS; DISEASE PHENOTYPES; INHERENT COMPLEXITY; MOLECULAR PROCESS; MULTIPARAMETERS; SINGLE CELLS; SUPER CELL;

T-CELLS;

ARTICLE; AUTOIMMUNE DISEASE; BEHCET DISEASE; CD8+ T LYMPHOCYTE; CELL NUCLEUS; CYTOLOGY; DIAGNOSTIC ACCURACY; DIAGNOSTIC PROCEDURE; FIBROBLAST; FLOW CYTOMETRY; GENE EXPRESSION; GENE SEQUENCE; HUMAN; PHENOTYPE; PREMATURE AGING; PROGERIA; SARCOIDOSIS; UVEITIS;

ARTIFICIAL INTELLIGENCE; DIAGNOSIS; HUMANS;

EID: 84884697284     PISSN: 1553734X     EISSN: 15537358     Source Type: Journal    
DOI: 10.1371/journal.pcbi.1003215     Document Type: Article

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