Aro: A machine learning approach to identifying single molecules and estimating classification error in fluorescence microscopy images

BMC Bioinformatics

Volumn 16, Issue 1, 2015, Pages

  Wu, Allison Chia Yi ^a   Rifkin, Scott A ^a  

^a UNIVERSITY OF CALIFORNIA   (United States)

Author keywords

Fluorescence microscopy; Image informatics; Image quality; Machine learning; Random forest; RNA; Single molecule imaging; smFISH

Indexed keywords

ARTIFICIAL INTELLIGENCE; BIOLOGY; CELL CULTURE; DECISION TREES; FLUORESCENCE; FLUORESCENCE MICROSCOPY; IMAGE QUALITY; LEARNING SYSTEMS; MOLECULES; NUCLEIC ACIDS; RNA;

FLUORESCENCE MICROSCOPY IMAGES; IMAGE INFORMATICS; MACHINE LEARNING APPROACHES; RANDOM FORESTS; SINGLE MOLECULE EXPERIMENTS; SINGLE MOLECULE FLUORESCENCE; SINGLE-MOLECULE IMAGING; SMFISH;

IMAGE CLASSIFICATION;

FLUORESCENT DYE; MESSENGER RNA;

ALGORITHM; ANIMAL; ANIMAL EMBRYO; ARTIFICIAL INTELLIGENCE; BIOLOGY; CAENORHABDITIS ELEGANS; COMPUTER PROGRAM; CYTOLOGY; EMBRYOLOGY; FLUORESCENCE; FLUORESCENCE IN SITU HYBRIDIZATION; FLUORESCENCE MICROSCOPY; IMAGE PROCESSING; METABOLISM; NANOTECHNOLOGY; PROCEDURES; STAINING;

ALGORITHMS; ANIMALS; ARTIFICIAL INTELLIGENCE; CAENORHABDITIS ELEGANS; COMPUTATIONAL BIOLOGY; EMBRYO, NONMAMMALIAN; FLUORESCENCE; FLUORESCENT DYES; IMAGE PROCESSING, COMPUTER-ASSISTED; IN SITU HYBRIDIZATION, FLUORESCENCE; MICROSCOPY, FLUORESCENCE; NANOTECHNOLOGY; RNA, MESSENGER; SOFTWARE; STAINING AND LABELING;

EID: 84930943472     PISSN: None     EISSN: 14712105     Source Type: Journal    
DOI: 10.1186/s12859-015-0534-z     Document Type: Article

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