Discovering collectively informative descriptors from high-throughput experiments

BMC Bioinformatics

Volumn 10, Issue , 2009, Pages

  Jeffries, Clark D ^a,b   Ward, William O ^c   Perkins, Diana O ^d   Wright, Fred A ^e  

^a University of North Carolina   (United States)

^b UNIVERSITY OF NORTH CAROLINA   (United States)

^c U S ENVIRONMENTAL PROTECTION AGENCY   (United States)

^d UNIVERSITY OF NORTH CAROLINA SCHOOL OF MEDICINE   (United States)

^e IL   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

COMPLEX DATASETS; CONTINGENCY TABLE; FISHER EXACT TEST; HIGH THROUGHPUT EXPERIMENTS; HIGH-THROUGHPUT TECHNOLOGIES; INFORMATIVE NESS; MINIMAL SUBSET; NOVEL ALGORITHM;

ALGORITHMS; COLLECTOR EFFICIENCY; EXPERIMENTS; RELIABILITY ANALYSIS;

THROUGHPUT;

MESSENGER RNA; PROTEIN;

ALGORITHM; ARTICLE; AUTOMATED PATTERN RECOGNITION; BIOLOGY; FACTUAL DATABASE; GENETICS; METABOLISM; METHODOLOGY;

ALGORITHMS; COMPUTATIONAL BIOLOGY; DATABASES, FACTUAL; PATTERN RECOGNITION, AUTOMATED; PROTEINS; RNA, MESSENGER;

EID: 76349112706     PISSN: None     EISSN: 14712105     Source Type: Journal    
DOI: 10.1186/1471-2105-10-431     Document Type: Article

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