Inferring adaptive regulation thresholds And association rules from gene expression data through combinatorial optimization learning

IEEE/ACM Transactions on Computational Biology and Bioinformatics

Volumn 4, Issue 4, 2007, Pages 624-633

  Ponzoni, Ignacio ^a   Azuaje, Francisco J ^b   Augusto, Juan Carlos ^b   Glass, David H ^b  

^a UNIVERSIDAD NACIONAL DEL SUR   (Argentina)

^b UNIVERSITY OF ULSTER   (United Kingdom)

Author keywords

Combinatorial optimization; Decision trees; Gene expression data; Genetic regulatory networks; Machine learning

Indexed keywords

ASSOCIATION RULES; COMBINATORIAL OPTIMIZATION; COMPUTATIONAL METHODS; DATA STRUCTURES; DECISION TREES; LEARNING SYSTEMS; YEAST;

ADAPTIVE REGULATION THRESHOLDS; GENE EXPRESSION DATA; GENE REGULATORY NETWORKS; LARGE SCALE INTEGRATIVE STUDIES; PUTATIVE REGULATORY ASSOCIATIONS;

GENE EXPRESSION;

SACCHAROMYCES CEREVISIAE;

FUNGAL PROTEIN;

ALGORITHM; ARTICLE; ARTIFICIAL INTELLIGENCE; AUTOMATED PATTERN RECOGNITION; BIOLOGY; CHEMISTRY; DNA MICROARRAY; FUNGAL GENE; GENE EXPRESSION; GENE EXPRESSION PROFILING; METABOLISM; METHODOLOGY; REPRODUCIBILITY; SACCHAROMYCES CEREVISIAE; STATISTICAL MODEL; THEORETICAL MODEL;

ALGORITHMS; ARTIFICIAL INTELLIGENCE; COMPUTATIONAL BIOLOGY; FUNGAL PROTEINS; GENE EXPRESSION; GENE EXPRESSION PROFILING; GENES, FUNGAL; MODELS, STATISTICAL; MODELS, THEORETICAL; OLIGONUCLEOTIDE ARRAY SEQUENCE ANALYSIS; PATTERN RECOGNITION, AUTOMATED; REPRODUCIBILITY OF RESULTS; SACCHAROMYCES CEREVISIAE;

EID: 36248985095     PISSN: 15455963     EISSN: None     Source Type: Journal    
DOI: 10.1109/tcbb.2007.1049     Document Type: Conference Paper

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