A novel knowledge-driven systems biology approach for phenotype prediction upon genetic intervention

IEEE/ACM Transactions on Computational Biology and Bioinformatics

Volumn 8, Issue 5, 2011, Pages 1170-1182

  Chang, Rui ^a   Shoemaker, Robert ^a   Wang, Wei ^a  

^a UNIVERSITY OF CALIFORNIA   (United States)

Author keywords

breast cancer; cell proliferation; Dynamic Bayesian network; genetic intervention; genetic network; phenotype prediction; systems biology

Indexed keywords

BREAST CANCER; DYNAMIC BAYESIAN NETWORK; GENETIC INTERVENTION; GENETIC NETWORK; PHENOTYPE PREDICTION; SYSTEMS BIOLOGY;

CELL PROLIFERATION; CELLS; DISEASES; DISTRIBUTED PARAMETER NETWORKS; DYNAMICS; FORECASTING; GENES; GENETIC ENGINEERING; GROWTH KINETICS; INFERENCE ENGINES; INTELLIGENT NETWORKS;

BAYESIAN NETWORKS;

TUMOR MARKER;

ANIMAL; ARTICLE; BAYES THEOREM; BIOLOGICAL MODEL; BREAST TUMOR; CELL GROWTH; COMPUTER SIMULATION; FEMALE; GENE REGULATORY NETWORK; GENETICS; HUMAN; METABOLISM; METHODOLOGY; MONTE CARLO METHOD; PATHOLOGY; PHENOTYPE; SIGNAL TRANSDUCTION; SYSTEMS BIOLOGY; TUMOR CELL LINE; TUMOR GENE;

ANIMALS; BAYES THEOREM; BREAST NEOPLASMS; CELL GROWTH PROCESSES; CELL LINE, TUMOR; COMPUTER SIMULATION; FEMALE; GENE REGULATORY NETWORKS; GENES, NEOPLASM; HUMANS; MODELS, GENETIC; MONTE CARLO METHOD; PHENOTYPE; SIGNAL TRANSDUCTION; SYSTEMS BIOLOGY; TUMOR MARKERS, BIOLOGICAL;

EID: 79960897495     PISSN: 15455963     EISSN: None     Source Type: Journal    
DOI: 10.1109/TCBB.2011.18     Document Type: Article

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