Scoring diverse cellular morphologies in image-based screens with iterative feedback and machine learning

Proceedings of the National Academy of Sciences of the United States of America

Volumn 106, Issue 6, 2009, Pages 1826-1831

  Jones, Thouis R ^a,b,c   Carpenter, Anne E ^a,b   Lamprecht, Michael R ^b   Moffat, Jason ^b,f   Silver, Serena J ^a   Grenier, Jennifer K ^a   Castoreno, Adam B ^d   Eggert, Ulrike S ^d   Root, David E ^a   Golland, Polina ^c   Sabatini, David M ^a,b,e  

^a BROAD INSTITUTE OF MIT AND HARVARD   (United States)

^b WHITEHEAD INSTITUTE FOR BIOMEDICAL RESEARCH   (United States)

^c MASSACHUSETTS INSTITUTE OF TECHNOLOGY   (United States)

^d HARVARD MEDICAL SCHOOL   (United States)

^e MASSACHUSETTS INSTITUTE OF TECHNOLOGY   (United States)

^f UNIVERSITY OF TORONTO   (Canada)

Author keywords

High content screening; High throughput image analysis; Phenotype

Indexed keywords

ANIMAL CELL; ARTICLE; CELL STRUCTURE; CONTROLLED STUDY; DROSOPHILA MELANOGASTER; FEEDBACK SYSTEM; HUMAN; HUMAN CELL; IMAGE ANALYSIS; MACHINE LEARNING; NONHUMAN; PHENOTYPE; PRIORITY JOURNAL; RNA INTERFERENCE; SCORING SYSTEM;

ALGORITHMS; ANIMALS; ARTIFICIAL INTELLIGENCE; CELLS; DIAGNOSTIC IMAGING; FEEDBACK; HUMANS; IMAGE CYTOMETRY; IMAGE INTERPRETATION, COMPUTER-ASSISTED; PATTERN RECOGNITION, AUTOMATED; PHENOTYPE; RNA INTERFERENCE; TISSUE ARRAY ANALYSIS;

EID: 60549104030     PISSN: 00278424     EISSN: 10916490     Source Type: Journal    
DOI: 10.1073/pnas.0808843106     Document Type: Article

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