Finding the "dark matter" in human and yeast protein network prediction and modelling

PLoS Computational Biology

Volumn 6, Issue 9, 2010, Pages

  Ranea, Juan A G ^a,b   Morilla, Ian ^b   Lees, Jon G ^a   Reid, Adam J ^a   Yeats, Corin ^a   Clegg, Andrew B ^a   Sanchez Jimenez, Francisca ^b   Orengo, Christine ^a  

^a UNIVERSITY COLLEGE LONDON   (United Kingdom)

^b UNIVERSITY OF MÁLAGA   (Spain)

Author keywords

[No Author keywords available]

Indexed keywords

BIOLOGICAL SYSTEMS; DESIGN OF EXPERIMENTS; PROTEINS; SIGNAL TRANSDUCTION; STATISTICS; TOPOLOGY; YEAST;

ACCURATE MODELING; DARK MATTER; FUNCTIONAL ASSOCIATIONS; HUMAN PROTEINS; MOLECULAR COMPONENTS; NETWORK MODELS; NETWORK PREDICTION; PROTEIN ASSOCIATIONS; PROTEIN NETWORK; YEAST PROTEINS;

FORECASTING;

PROTEOME; RAS PROTEIN; TRANSCRIPTION FACTOR GATA 3;

ACCURACY; ARTICLE; BAYESIAN LEARNING; COMPUTER MODEL; COMPUTER PREDICTION; CONTROLLED STUDY; GENE REGULATORY NETWORK; HUMAN; INTEGRATION; NONHUMAN; NORMAL DISTRIBUTION; PROTEIN ANALYSIS; PROTEIN DOMAIN; PROTEIN FUNCTION; PROTEIN LOCALIZATION; PROTEIN PROTEIN INTERACTION; QUALITY CONTROL; RECALL; SIGNAL TRANSDUCTION; SYSTEMS BIOLOGY; YEAST;

EUKARYOTA;

EID: 77957806312     PISSN: 1553734X     EISSN: 15537358     Source Type: Journal    
DOI: 10.1371/journal.pcbi.1000945     Document Type: Article

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