A knowledge-driven probabilistic framework for the prediction of protein-protein interaction networks

Computers in Biology and Medicine

Volumn 40, Issue 3, 2010, Pages 306-317

  Browne, Fiona ^a   Wang, Haiying ^a   Zheng, Huiru ^a   Azuaje, Francisco ^b  

^a UNIVERSITY OF ULSTER   (United Kingdom)

^b LUXEMBOURG INSTITUTE OF HEALTH   (Luxembourg)

Author keywords

"Omic" datasets; Computational systems biology; Functional genomics; Machine and statistical learning; Protein protein interaction networks

Indexed keywords

BAYESIAN NETWORKS; DATA INTEGRATION; FORECASTING;

COMPUTATIONAL SYSTEMS BIOLOGY; FUNCTIONAL GENOMICS; OMIC DATASETS; PROTEIN-PROTEIN INTERACTION NETWORKS; STATISTICAL LEARNING;

PROTEINS;

ALGORITHM; ANALYTIC METHOD; AREA UNDER THE CURVE; ARTICLE; BAYESIAN LEARNING; BIOINFORMATICS; CALCULATION; CONTROLLED STUDY; GENETIC VARIABILITY; KNOWLEDGE DRIVEN BAYESIAN NETWORK; MATHEMATICAL MODEL; PREDICTION; PRIORITY JOURNAL; PROBABILITY; PROTEIN PROTEIN INTERACTION; QUALITATIVE ANALYSIS; QUANTITATIVE ANALYSIS; RECEIVER OPERATING CHARACTERISTIC; VALIDATION PROCESS; YEAST;

EID: 77549085334     PISSN: 00104825     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.compbiomed.2010.01.002     Document Type: Article

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