Gene expression predictors of breast cancer outcomes.

By Erich Huang, S. H. Cheng, H. Dressman, J. Pittman, M. Tsou, C. Horng, A. Bild, E. Iversen, Ming Liao, C. Chen and 3 more. The Lancet, 2003

673 citations recorded by the source corpus.

Subfields: Cancer Research, Molecular Biology

View Paper: https://www.semanticscholar.org/paper/1b88b5d3c2470db4315fd77233be60672c79bbed

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Cancer Research

1. Ian O. Ellis
2. Joseph R. Nevins
3. Charles M. Perou
4. Arul M. Chinnaiyan
5. E. Miska
6. Manel Esteller

In Molecular Biology

1. Arul M. Chinnaiyan
2. Debashis Ghosh
3. Daniel R. Rhodes
4. Carlos M. Carvalho
5. Nuno L. Barbosa-Morais
6. Carlos Caldas
7. Mark A. Rubin
8. Joseph R. Nevins

Citations in the corpus

• MicroRNA expression profiling of human breast cancer identifies new markers of tumor subtype (2007, background): “A number of classifiers have been developed for human breast tumors, including the use of expression signatures as prognostic tools [65-75].”
• Coexpression analysis of human genes across many microarray data sets. (2004, neutral): “2002) 30 5418 60268 (Huang et al. 2003) 89 8257 137512 (van’t Veer et al.”
• High-Dimensional Sparse Factor Modeling: Applications in Gene Expression Genomics (2008, background): “…u95av2 microarray profiles on three sets of breast cancer samples: 138 tumor samples from the Taiwanese–U.S. CODEx study (Huang, West, and Nevins 2002; Huang et al. 2003; Nevins et al. 2003; Pittman et al. 2004), 74 additional samples from the same center collected a year or two later, and 83…”
• Molecular classification of head and neck squamous cell carcinomas using patterns of gene expression. (2004, methodology): “These data suggest that Group 1 patients should be evaluated for benefits from EGFR inhibitor(Huang et al., 2003) and demonstrated that the 11 genes shown in red in Figure 4B were exactly shared between our study and containing treatment regimens.”
• Integrative genomic and proteomic analysis of prostate cancer reveals signatures of metastatic progression. (2005, methodology): “Six different cancer profiling studies (Bhattacharjee et al., 2001; Freije et al., 2004; Glinsky et al., 2004; Huang et al., 2003; van ’t Veer et al., 2002; Yu et al., 2004) were used for evaluation of prognostic value of these concordant genes.”
• Sparse graphical models for exploring gene expression data (2004, neutral): “We have developed the analysis on expression data from 158 breast cancer samples arising in our studies of molecular phenotyping for clinical prediction [15,16,28].”