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H-CORE: enabling genome-scale Bayesian analysis of biological systems without prior knowledge.
Biosystems. 2007 Jul-Aug;90(1):197-210. doi: 10.1016/j.biosystems.2006.08.004. Epub 2006 Aug 22.
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A probabilistic methodology for integrating knowledge and experiments on biological networks.
J Comput Biol. 2006 Mar;13(2):165-81. doi: 10.1089/cmb.2006.13.165.
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Functional stoichiometric analysis of metabolic networks.
Bioinformatics. 2005 Nov 15;21(22):4176-80. doi: 10.1093/bioinformatics/bti674. Epub 2005 Sep 27.
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SimBioNeT: a simulator of biological network topology.
IEEE/ACM Trans Comput Biol Bioinform. 2012;9(2):592-600. doi: 10.1109/TCBB.2011.116. Epub 2011 Aug 18.
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A lock-and-key model for protein-protein interactions.
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Genetdes: automatic design of transcriptional networks.
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Sustainable software development in science - insights from 20 years of Vanted.
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Assembling Disease Networks From Causal Interaction Resources.
Front Genet. 2021 Jun 11;12:694468. doi: 10.3389/fgene.2021.694468. eCollection 2021.
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An overview of bioinformatics methods for modeling biological pathways in yeast.
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Discrete Logic Modelling Optimization to Contextualize Prior Knowledge Networks Using PRUNET.
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A statistical framework for revealing signaling pathways perturbed by DNA variants.
Nucleic Acids Res. 2015 Jun 23;43(11):e74. doi: 10.1093/nar/gkv203. Epub 2015 Mar 12.
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DEGAS: de novo discovery of dysregulated pathways in human diseases.
PLoS One. 2010 Oct 19;5(10):e13367. doi: 10.1371/journal.pone.0013367.
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Logic-based models for the analysis of cell signaling networks.
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IRIS: a method for reverse engineering of regulatory relations in gene networks.
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Structural and functional analysis of cellular networks with CellNetAnalyzer.
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Refinement and expansion of signaling pathways: the osmotic response network in yeast.
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Integrative analysis of genome-wide experiments in the context of a large high-throughput data compendium.
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BIOCHAM: an environment for modeling biological systems and formalizing experimental knowledge.
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A probabilistic methodology for integrating knowledge and experiments on biological networks.
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