A Bayesian Networks Approach for Predicting Protein-Protein Interactions from Genomic Data

By R. Jansen, Haiyuan Yu, D. Greenbaum, Yuval Kluger, Nevan J. Krogan, Sambath Chung, Andrew Emili, M. Snyder, Jack Greenblatt, Mark Gerstein. 2003

1,283 citations recorded by the source corpus.

Subfields: Molecular Biology

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Citations in the corpus

Black-Box vs. White-Box: Understanding Their Advantages and Weaknesses From a Practical Point of View (2019, neutral): “Some authors [85] can consider BN-based models as an”
Inferring networks of diffusion and influence (2010, neutral): “Our work is also related to a link prediction problem [Jansen et al. 2003; Taskar et al. 2003; Liben-Nowell and Kleinberg 2003; Backstrom and Leskovec 2011; Vert and Yamanishi 2005] but different in a sense that this line of work assumes that part of the network is already visible to us.”
How complete are current yeast and human protein-interaction networks? (2006, methodology): “The false-positive rate of the computationally derived Jansen dataset [22] was determined by comparing it to Gavin et al. [27] and Krogan et al .”
Predicting protein-protein interactions using signature products (2005, methodology): “Having achieved reasonably good results on our first two benchmarks, we decided to attempt a comparison with the work done in Jansen et al. (2003). Although the approach in Jansenet al.”
Annotation transfer between genomes: protein-protein interologs and protein-DNA regulogs. (2004, methodology): “Previously, Jansen et al. (2003) considered pairs of proteins in different subcellular compartments as good estimates for non-interacting pairs (Kumar et al. 2002).”
Structure-based prediction of protein-protein interactions on a genome-wide scale (2012, methodology): “25 and corresponding in our study to a FPR of , 0.1%; see Methods), about 70% and 50%, respectively, share GO biological terms at, or more specific than, the sixth level of the GO hierarchy, suggesting that many of these interactions are real (Supplementary Fig.”
A protein-protein interaction network for human inherited ataxias and disorders of Purkinje cell degeneration. (2006, methodology): “To determine whether the protein–protein interactions in our network are biologically relevant (Jansen et al., 2003; Jansen and Gerstein, 2004; Rual et al., 2005), we used informatic approaches.”
Disease candidate gene identification and prioritization using protein interaction networks (2009, positive): “…been developed with a number of goals [9], including protein function prediction [10], identification of functional modules [11], interaction prediction [12,13], identification of disease candidate genes [14,15] and drug targets [16,17], and the study of network structure and evolution [18-22].”
The Bayesian revolution in genetics (2004, background): “Another burgeoning area, not covered in this review, is the use of Bayesian networks for combining the results from different analyses on the same data set”
Sparse graphical Gaussian modeling of the isoprenoid gene network in Arabidopsis thaliana (2004, methodology): “In simple eukaryotes or prokaryotes, gene-expression data has been combined with two-hybrid data [1] and phenotypic data [2] to successfully predict protein-protein interaction and transcriptional regulation on a large scale.”
Deciphering Protein–Protein Interactions. Part II. Computational Methods to Predict Protein and Domain Interaction Partners (2007, methodology): “Different classification methods have been successfully applied to the prediction of protein and domain interactions [41–54].”