@article{
 title = {Inferring cellular networks using probabilistic graphical models.},
 type = {article},
 year = {2004},
 identifiers = {[object Object]},
 keywords = {Bayes Theorem,Cell Physiological Phenomena,Computational Biology,Gene Expression,Gene Expression Profiling,Gene Expression Regulation,Mathematics,Models, Biological,Models, Genetic,Models, Statistical},
 pages = {799-805},
 volume = {303},
 websites = {http://www.ncbi.nlm.nih.gov/pubmed/14764868},
 month = {2},
 day = {6},
 id = {37b9e804-1abd-3d46-b09f-bf4892ce5220},
 created = {2016-04-27T01:35:32.000Z},
 accessed = {2016-04-27},
 file_attached = {false},
 profile_id = {95ad1c8a-ddc5-3572-ab20-1b5660ace0c4},
 group_id = {409b6953-b59c-338c-b6e7-a89d45a37162},
 last_modified = {2016-04-27T01:35:32.000Z},
 read = {false},
 starred = {false},
 authored = {false},
 confirmed = {true},
 hidden = {false},
 abstract = {High-throughput genome-wide molecular assays, which probe cellular networks from different perspectives, have become central to molecular biology. Probabilistic graphical models are useful for extracting meaningful biological insights from the resulting data sets. These models provide a concise representation of complex cellular networks by composing simpler submodels. Procedures based on well-understood principles for inferring such models from data facilitate a model-based methodology for analysis and discovery. This methodology and its capabilities are illustrated by several recent applications to gene expression data.},
 bibtype = {article},
 author = {Friedman, Nir},
 journal = {Science (New York, N.Y.)},
 number = {5659}
}

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