A statistical method to incorporate biological knowledge for generating testable novel gene regulatory interactions from microarray experiments

BMC Bioinformatics

Volumn 8, Issue , 2007, Pages

  Larsen, Peter ^a   Almasri, Eyad ^b   Chen, Guanrao ^c   Dai, Yang ^b  

^a UNIVERSITY OF ILLINOIS AT CHICAGO   (United States)

^b University of Illinois at Chicago   (United States)

^c 1120 Science and Engineering Offices   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

BIOLOGICAL EXPERIMENTS; HYPOTHESIS GENERATION; INTERACTION NETWORKS; MICROARRAY EXPERIMENTS; PEARSON CORRELATION COEFFICIENTS; REGULATORY INTERACTIONS; STATISTICAL ANALYSIS METHODS; YEAST SACCHAROMYCES CEREVISIAE;

CORRELATION METHODS; EXPERIMENTS; GENES; YEAST;

TRANSCRIPTION;

ARTICLE; CELL CYCLE; CELL DIVISION; CELL SYNCHRONIZATION; COMPUTER PROGRAM; CORRELATION COEFFICIENT; GENE EXPRESSION PROFILING; GENE INTERACTION; GENETIC SCREENING; KNOWLEDGE BASE; MEDLINE; MICROARRAY ANALYSIS; POLYMERASE CHAIN REACTION; SACCHAROMYCES CEREVISIAE; SCORING SYSTEM; STATISTICAL ANALYSIS; VALIDATION STUDY; BIOLOGY; DNA MICROARRAY; EVALUATION; GENE REGULATORY NETWORK; GENETIC DATABASE; GENETICS; METHODOLOGY;

SACCHAROMYCES CEREVISIAE;

COMPUTATIONAL BIOLOGY; DATA INTERPRETATION, STATISTICAL; DATABASES, GENETIC; GENE EXPRESSION PROFILING; GENE REGULATORY NETWORKS; OLIGONUCLEOTIDE ARRAY SEQUENCE ANALYSIS; PUBMED; SACCHAROMYCES CEREVISIAE;

EID: 36349005434     PISSN: None     EISSN: 14712105     Source Type: Journal    
DOI: 10.1186/1471-2105-8-317     Document Type: Article

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