Empirical Bayes conditional independence graphs for regulatory network recovery

Computational inference methods that make use of graphical models to extract regulatory networks from gene expression data can have difficulty reconstructing dense regions of a network, a consequence of both computational complexity and unreliable parameter estimation when sample size is small. As a...

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Veröffentlicht in:	Bioinformatics 2012-08, Vol.28 (15), p.2029-2036
Hauptverfasser:	MAHDI, Rami, MADDURI, Abishek S, GUOQING WANG, STRULOVICI-BAREL, Yael, SALIT, Jacqueline, HACKETT, Neil R, CRYSTAL, Ronald G, MEZEY, Jason G
Format:	Artikel
Sprache:	eng
Schlagworte:	Algorithms Bayes Theorem Biological and medical sciences Computational Biology - methods Computer Simulation Fundamental and applied biological sciences. Psychology Gene Expression Profiling - methods Gene Regulatory Networks General aspects Humans Mathematics in biology. Statistical analysis. Models. Metrology. Data processing in biology (general aspects) Original Papers Respiratory Mucosa - metabolism Software
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