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IEAAIE
2010
Springer
13 years 2 months ago
Constructive Neural Networks to Predict Breast Cancer Outcome by Using Gene Expression Profiles
Abstract. Gene expression profiling strategies have attracted considerable interest from biologist due to the potential for high throughput analysis of hundreds of thousands of gen...
Daniel Urda, José Luis Subirats, Leonardo F...
BIOINFORMATICS
2005
134views more  BIOINFORMATICS 2005»
13 years 4 months ago
Outcome signature genes in breast cancer: is there a unique set?
Motivation: direct bearing whose expres Sorlie et al., 2 gene sets is a diseases (Lossos et al., 2004; Miklos and Maleszka, 2004), and the variables that could account questions i...
Liat Ein-Dor, Itai Kela, Gad Getz, David Givol, Ey...
BMCBI
2010
96views more  BMCBI 2010»
13 years 4 months ago
Incorporating gene co-expression network in identification of cancer prognosis markers
Background: Extensive biomedical studies have shown that clinical and environmental risk factors may not have sufficient predictive power for cancer prognosis. The development of ...
Shuangge Ma, Mingyu Shi, Yang Li, Danhui Yi, Ben-C...
BMCBI
2006
123views more  BMCBI 2006»
13 years 4 months ago
Characterizing disease states from topological properties of transcriptional regulatory networks
Background: High throughput gene expression experiments yield large amounts of data that can augment our understanding of disease processes, in addition to classifying samples. He...
David Tuck, Harriet Kluger, Yuval Kluger
BMCBI
2004
180views more  BMCBI 2004»
13 years 4 months ago
Noise filtering and nonparametric analysis of microarray data underscores discriminating markers of oral, prostate, lung, ovaria
Background: A major goal of cancer research is to identify discrete biomarkers that specifically characterize a given malignancy. These markers are useful in diagnosis, may identi...
Virginie M. Aris, Michael J. Cody, Jeff Cheng, Jam...