Network-based de-noising improves prediction from microarray data

BMC Bioinformatics

Volumn 7, Issue SUPPL.1, 2006, Pages

  Kato, Tsuyoshi ^a,b   Murata, Yukio ^c   Miura, Koh ^c   Asai, Kiyoshi ^a,b   Horton, Paul B ^b   Tsuda, Koji ^b   Fujibuchi, Wataru ^b  

^a UNIVERSITY OF TOKYO   (Japan)

^b NATIONAL INSTITUTE OF ADVANCED INDUSTRIAL SCIENCE AND TECHNOLOGY AIST   (Japan)

^c TOHOKU UNIVERSITY GRADUATE SCHOOL OF MEDICINE   (Japan)

Author keywords

[No Author keywords available]

Indexed keywords

CROSS VALIDATION; EXTENDED VERSIONS; GENE EXPRESSION DATA; NOISE REDUCTION METHODS; PEARSON'S CORRELATION COEFFICIENTS; PREDICTION PERFORMANCE; PROTEIN-PROTEIN INTERACTIONS; SEQUENCE SIMILARITY;

CORRELATION METHODS; DISEASES; GENE EXPRESSION; HETEROGENEOUS NETWORKS; INPUT OUTPUT PROGRAMS; MICROARRAYS; NOISE ABATEMENT; PROTEINS;

FORECASTING;

ANTINEOPLASTIC AGENT;

ARTICLE; CONTROLLED STUDY; CORRELATION COEFFICIENT; GENE EXPRESSION; GENETIC HETEROGENEITY; HUMAN; HUMAN CELL; MICROARRAY ANALYSIS; NOISE REDUCTION; POLYMERASE CHAIN REACTION; PREDICTION; PROTEIN INTERACTION; SEQUENCE HOMOLOGY; VALIDATION PROCESS; ALGORITHM; ARTIFICIAL NEURAL NETWORK; DNA MICROARRAY; GENE EXPRESSION REGULATION; PRINCIPAL COMPONENT ANALYSIS; PROCEDURES; PROTEIN ANALYSIS; PROTEIN DATABASE; REGRESSION ANALYSIS; REPRODUCIBILITY; SEQUENCE ANALYSIS; STATISTICAL ANALYSIS; STATISTICAL MODEL;

ALGORITHMS; DATA INTERPRETATION, STATISTICAL; DATABASES, PROTEIN; GENE EXPRESSION REGULATION, NEOPLASTIC; HUMANS; MODELS, STATISTICAL; NEURAL NETWORKS (COMPUTER); OLIGONUCLEOTIDE ARRAY SEQUENCE ANALYSIS; PRINCIPAL COMPONENT ANALYSIS; PROTEIN INTERACTION MAPPING; REGRESSION ANALYSIS; REPRODUCIBILITY OF RESULTS; SEQUENCE ANALYSIS, PROTEIN;

EID: 33947304179     PISSN: None     EISSN: 14712105     Source Type: Journal    
DOI: 10.1186/1471-2105-7-S1-S4     Document Type: Article

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