Live-cell phenotypic-biomarker microfluidic assay for the risk stratification of cancer patients via machine learning

Nature Biomedical Engineering

Volumn 2, Issue 10, 2018, Pages 761-772

  Manak, Michael S ^a   Varsanik, Jonathan S ^a   Hogan, Brad J ^a   Whitfield, Matt J ^a   Su, Wendell R ^a   Joshi, Nikhil ^a   Steinke, Nicolai ^a   Min, Andrew ^a   Berger, Delaney ^a   Saphirstein, Robert J ^a   Dixit, Gauri ^a   Meyyappan, Thiagarajan ^a   Chu, Hui May ^b   Knopf, Kevin B ^c   Albala, David M ^d   Sant, Grannum R ^e   Chander, Ashok C ^a  

^a Cellanyx Diagnostics   (United States)

^b Massachusetts   (United States)

^c Cancer Commons   (United States)

^d New York Medical School   (United States)

^e TUFTS UNIVERSITY SCHOOL OF MEDICINE   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

ARTIFICIAL INTELLIGENCE; BIOMARKERS; CELLS; COMPUTER VISION; CYTOLOGY; LEARNING ALGORITHMS; LEARNING SYSTEMS; MICROFLUIDICS; PATHOLOGY; SURGERY; TISSUE; UROLOGY;

AREA UNDER THE CURVES; EXTRACELLULAR MATRICES; MACHINE VISION ANALYSIS; MICRO-FLUIDIC DEVICES; MICROFLUIDIC ASSAYS; RECEIVER OPERATING CHARACTERISTIC CURVES; RISK STRATIFICATION; SINGLE CELL RESOLUTION;

DISEASES;

EID: 85053806353     PISSN: None     EISSN: 2157846X     Source Type: Journal    
DOI: 10.1038/s41551-018-0285-z     Document Type: Article

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