Network modeling links breast cancer susceptibility and centrosome dysfunction.

By M. Pujana, Jing-Dong J. Han, Lea M. Starita, Kristen Stevens, Muneesh Tewari, J. S. Ahn, G. Rennert, V. Moreno, T. Kirchhoff, B. Gold and 24 more. 2007

637 citations recorded by the source corpus.

Subfields: Genetics, Molecular Biology

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Citations in the corpus

EgoNet: identification of human disease ego-network modules (2014, methodology): “To date, many computational methods have been developed to integrate gene expression profiles with protein-protein interaction maps or pathway databases, with the goal of identifying significant subnetwork markers for predicting biological or clinical outcomes [10-18].”
Tree-Guided Group Lasso for Multi-Task Regression with Structured Sparsity (2009, background): “ each other [Cheung et al. (2005), Stranger et al. (2005), Zhu et al. (2008)]. However, itis widelybelieved that manyof thegenes in the same biological pathway are often co-expressed or co-regulated [Pujana et al. (2007), Zhang and Horvath (2005)] and may share a common genetic basis that causes the”
A Dynamic Network Approach for the Study of Human Phenotypes (2009, background): “Moreover, in the gene expression front, microarray expression profiles and other cellular level information have been used to explore networks in inflammation [7], breast cancer [8], and brain disease [9].”
Long noncoding RNA high expression in hepatocellular carcinoma facilitates tumor growth through enhancer of zeste homolog 2 in humans (2011, methodology): “We present gene-coexpression networks to identify interactions among genes.(17) Gene-coexpression networks were built according to the normalized signal intensity of specific expressed genes.”
Ploidy and large-scale genomic instability consistently identify basal-like breast carcinomas with BRCA1/2 inactivation. (2012, neutral): “considering the numerous reports linking BRCA1 to centrosome structure and functions, and experimentally demonstrating aneuploidy and centrosome amplification as a consequence of BRCA1 inactivation (45).”
Protein networks in disease. (2008, background): “In a conceptually similar study (Pujana et al. 2007), a breast cancer-related network was constructed starting with four known breast cancer-associated genes: BRCA1, BRCA2, ATM, and CHEK2.”
Interactions between Hyaluronan and Its Receptors (CD44, RHAMM) Regulate the Activities of Inflammation and Cancer (2015, background): “In addition, disruption of AURKA (274), BRCA1 (275), or RHAMM (276) modifies neurite extension, an alternate differentiation program dependent on microtubule nucleation (267) (Figure 5).”
Structure and dynamics of molecular networks: A novel paradigm of drug discovery: A comprehensive review (2012, background): “…datasets, such as protein domain interactions, gene ontology annotations, cancer-related mutations, or cancer prognosis information refined predictions further (Franke et al., 2006; Pujana et al., 2007; Chang et al., 2009; Lee et al., 2009; Wu et al., 2010; Xiong et al., 2010; Yeh et al., 2012).”