Gene-expression profiles predict survival of patients with lung adenocarcinoma
By D. Beer, S. Kardia, Chiang-Ching Huang, T. Giordano, A. Levin, David E. Misek, Lin Lin, Guoan Chen, Tarek G Gharib, Dafydd G. Thomas and 7 more. Nature Medicine, 2002
2,018 citations recorded by the source corpus.
Subfields: Molecular Biology, Pulmonary and Respiratory Medicine
View Paper: https://www.semanticscholar.org/paper/13bae0189ceed3604252aa9e14951fb4feb960f9
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Molecular Biology
- Jianguo Xia
- Purvesh Khatri
- Trey Ideker
- Balázs Győrffy
- Aviv Regev
- Nir Friedman
- Arthur Gretton
- Jieping Ye
In Pulmonary and Respiratory Medicine
- Balázs Győrffy
- A. Lánczky
- Sun-Yuan Kung
- Kenta Nakai
- Akinori Sarai
- Hideaki Mizuno
- K. Kitada
- Harikrishna Nakshatri
Citations in the corpus
- Online Survival Analysis Software to Assess the Prognostic Value of Biomarkers Using Transcriptomic Data in Non-Small-Cell Lung Cancer (2013, background): “While numerous individual genes associated with survival have been published in the last thirty years, new microarray-based multigene molecular prognostic models using genomic signatures have only emerged in the last ten years [3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19].”
- Kernel Methods and Machine Learning (2014, background): “Assume we have initially that ŵ0 = [11]T and also that ρ0 = 0.”
- A gene expression signature associated with "K-Ras addiction" reveals regulators of EMT and tumor cell survival. (2009, background): “Cancer versus normal Lung type: top 10% overexpressed in lung adenocarcinoma (Beer et al., 2002) 14 4.”
- Feature selection for high-dimensional class-imbalanced data sets using Support Vector Machines (2014, background): “The lung cancer data set contains the gene expression of 181 samples (31 malignant and 150 normal) described by 12533 features [4].”
- GAGE: generally applicable gene set enrichment for pathway analysis (2009, positive): “As a test case, we applied GAGE, PAGE and GSEA to two lung cancer datasets [21,22] which were originally analyzed and compared by GSEA [3].”
- MicroRNA signature predicts survival and relapse in lung cancer. (2008, background): “In protein-coding gene expression profiling, the genes associated with patient survival can vary substantially from study to study, with few genes being consistently reported in different lung cancer studies (Beer et al., 2002; Potti et al., 2006).”