Genetic interaction motif finding by expectation maximization - A novel statistical model for inferring gene modules from synthetic lethality

BMC Bioinformatics

Volumn 6, Issue , 2005, Pages

  Qi, Yan ^a,b   Ye, Ping ^a,b   Bader, Joel S ^a,b  

^a Johns Hopkins University   (United States)

^b JOHNS HOPKINS UNIVERSITY SCHOOL OF MEDICINE   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

EXPECTATION - MAXIMIZATIONS; EXPERIMENTAL EVIDENCE; FUNCTIONAL ASSOCIATIONS; HETEROGENEOUS DATASETS; INTEGRATIVE ANALYSIS; NONLINEAR TRANSFORMS; PROBABILISTIC ALGORITHM; PROBABILISTIC FRAMEWORK;

ALGORITHMS; ASSOCIATION REACTIONS; COMPLEX NETWORKS; MAXIMUM PRINCIPLE; PROTEINS; YEAST;

GENES;

ARTICLE; COMPUTER NETWORK; FUNGAL GENE; GENE IDENTIFICATION; GENE INTERACTION; GENETIC ALGORITHM; LETHALITY; MATHEMATICAL COMPUTING; MATHEMATICAL MODEL; NONHUMAN; PROBABILITY; PROCESS OPTIMIZATION; PROTEIN MOTIF; ALGORITHM; BIOLOGICAL MODEL; BIOLOGY; COMPUTER PROGRAM; GENE EXPRESSION PROFILING; GENOMICS; METABOLISM; PROCEDURES; SACCHAROMYCES CEREVISIAE; STATISTICAL ANALYSIS; STATISTICAL MODEL;

SACCHAROMYCES CEREVISIAE; SACCHAROMYCETALES;

SACCHAROMYCES CEREVISIAE PROTEIN;

ALGORITHMS; AMINO ACID MOTIFS; COMPUTATIONAL BIOLOGY; DATA INTERPRETATION, STATISTICAL; GENE EXPRESSION PROFILING; GENES, FUNGAL; GENOMICS; MODELS, GENETIC; MODELS, STATISTICAL; PROBABILITY; SACCHAROMYCES CEREVISIAE; SACCHAROMYCES CEREVISIAE PROTEINS; SOFTWARE;

EID: 29244482771     PISSN: 14712105     EISSN: 14712105     Source Type: Journal    
DOI: 10.1186/1471-2105-6-288     Document Type: Article

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