Seq-ing improved gene expression estimates from microarrays using machine learning

BMC Bioinformatics

Volumn 16, Issue 1, 2015, Pages

  Korir, Paul K ^a   Geeleher, Paul ^b   Seoighe, Cathal ^c  

^a UNIVERSITY COLLEGE CORK   (Ireland)

^b UNIVERSITY OF CHICAGO   (United States)

^c School of Mathematics Statistics and Applied Mathematics ^*  (South Africa)

Author keywords

Machine learning; Microarray; RNA Seq; Statistical learning

Indexed keywords

ARTIFICIAL INTELLIGENCE; BIOASSAY; DECISION TREES; GENES; LEARNING SYSTEMS; MICROARRAYS; RNA; TISSUE;

EXPRESSION LEVELS; FLUORESCENCE INTENSITIES; MICROARRAY ANALYSIS; MICROARRAY DATA; PUBLIC REPOSITORIES; STATISTICAL LEARNING; TISSUE EXPRESSION; TRANSCRIPT LEVEL;

GENE EXPRESSION;

TRANSCRIPTOME;

BIOLOGY; GENE EXPRESSION PROFILING; HIGH THROUGHPUT SEQUENCING; HUMAN; MACHINE LEARNING; PROCEDURES; SEQUENCE ANALYSIS; STANDARDS;

COMPUTATIONAL BIOLOGY; GENE EXPRESSION PROFILING; HIGH-THROUGHPUT NUCLEOTIDE SEQUENCING; HUMANS; MACHINE LEARNING; SEQUENCE ANALYSIS, RNA; TRANSCRIPTOME;

EID: 84940886273     PISSN: None     EISSN: 14712105     Source Type: Journal    
DOI: 10.1186/s12859-015-0712-z     Document Type: Article

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