Machine learning approaches to supporting the identification of photoreceptor-enriched genes based on expression data

BMC Bioinformatics

Volumn 7, Issue , 2006, Pages

  Wang, Haiying ^a   Zheng, Huiru ^a   Simpson, David ^b   Azuaje, Francisco ^a  

^a UNIVERSITY OF ULSTER   (United Kingdom)

^b QUEEN'S UNIVERSITY BELFAST   (United Kingdom)

Author keywords

[No Author keywords available]

Indexed keywords

EXPERIMENTAL APPROACHES; FUNCTIONAL ASSOCIATIONS; FUNCTIONAL PREDICTIONS; LARGE-SCALE ANALYSIS; MACHINE LEARNING APPROACHES; SERIAL ANALYSIS OF GENE EXPRESSIONS; SUPERVISED CLASSIFICATION; TRANSCRIPTIONAL REGULATORY NETWORKS;

CELLS; CLUSTER ANALYSIS; COMPLEX NETWORKS; CYTOLOGY; LEARNING SYSTEMS; MAMMALS; MATHEMATICAL MODELS; OPHTHALMOLOGY; VISION;

GENE EXPRESSION;

ALGORITHM; ARTICLE; ARTIFICIAL INTELLIGENCE; CLASSIFICATION; CLUSTER ANALYSIS; COMPUTER MODEL; CONTROLLED STUDY; DATA ANALYSIS; DEVELOPMENTAL STAGE; GENE EXPRESSION; GENE EXPRESSION PROFILING; GENE FUNCTION; GENE IDENTIFICATION; METHODOLOGY; MODEL; MOUSE; NONHUMAN; PHOTORECEPTOR; PREDICTION; RANDOMIZATION; RETINA DEVELOPMENT; SAMPLING; SERIAL ANALYSIS OF GENE EXPRESSION; VALIDATION PROCESS; ANIMAL; AUTOMATED PATTERN RECOGNITION; BIOLOGICAL MODEL; COMPUTER SIMULATION; EVALUATION STUDY; FEASIBILITY STUDY; HUMAN; METABOLISM; PHOTORECEPTOR CELL; PHYSIOLOGY; PROCEDURES; SIGNAL TRANSDUCTION;

MAMMALIA;

EYE PROTEIN; TRANSCRIPTION FACTOR;

ANIMALS; ARTIFICIAL INTELLIGENCE; COMPUTER SIMULATION; EYE PROTEINS; FEASIBILITY STUDIES; GENE EXPRESSION PROFILING; HUMANS; MODELS, BIOLOGICAL; PATTERN RECOGNITION, AUTOMATED; PHOTORECEPTOR CELLS; SIGNAL TRANSDUCTION; TRANSCRIPTION FACTORS;

EID: 33645469412     PISSN: 14712105     EISSN: 14712105     Source Type: Journal    
DOI: 10.1186/1471-2105-7-116     Document Type: Article

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