Related references
Note: Only part of the references are listed.Robust biomarker identification for cancer diagnosis with ensemble feature selection methods
Thomas Abeel et al.
BIOINFORMATICS (2010)
Prediction of breast cancer prognosis using gene set statistics provides signature stability and biological context
Gad Abraham et al.
BMC BIOINFORMATICS (2010)
Stability selection
Nicolai Meinshausen et al.
JOURNAL OF THE ROYAL STATISTICAL SOCIETY SERIES B-STATISTICAL METHODOLOGY (2010)
Functional analysis of multiple genomic signatures demonstrates that classification algorithms choose phenotype-related genes
W. Shi et al.
PHARMACOGENOMICS JOURNAL (2010)
Predicting prognosis of breast cancer with gene signatures: are we lost in a sea of data?
Takayuki Iwamoto et al.
GENOME MEDICINE (2010)
MOLECULAR ORIGINS OF CANCER Gene-Expression Signatures in Breast Cancer
Christos Sotiriou et al.
NEW ENGLAND JOURNAL OF MEDICINE (2009)
NCBI GEO: archive for high-throughput functional genomic data
Tanya Barrett et al.
NUCLEIC ACIDS RESEARCH (2009)
Pathway analysis reveals functional convergence of gene expression profiles in breast cancer
Ronglai Shen et al.
BMC MEDICAL GENOMICS (2008)
Meta-analysis of gene expression profiles in breast cancer: toward a unified understanding of breast cancer subtyping and prognosis signatures
Pratyaksha Wirapati et al.
BREAST CANCER RESEARCH (2008)
A comprehensive analysis of prognostic signatures reveals the high predictive capacity of the Proliferation, Immune response and RNA splicing modules in breast cancer
Fabien Reyal et al.
BREAST CANCER RESEARCH (2008)
A new method to measure the semantic similarity of GO terms
James Z. Wang et al.
BIOINFORMATICS (2007)
Genetic reclassification of histologic grade delineates new clinical subtypes of breast cancer
Anna V. Ivshina et al.
CANCER RESEARCH (2006)
Concordance among gene-expression-based predictors for breast cancer
Cheng Fan et al.
NEW ENGLAND JOURNAL OF MEDICINE (2006)
A comparison of univariate and multivariate gene selection techniques for classification of cancer datasets
Carmen Lai et al.
BMC BIOINFORMATICS (2006)
Thousands of samples are needed to generate a robust gene list for predicting outcome in cancer
L Ein-Dor et al.
PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA (2006)
Gene expression profiling in breast cancer: Understanding the molecular basis of histologic grade to improve prognosis
C Sotiriou et al.
JNCI-JOURNAL OF THE NATIONAL CANCER INSTITUTE (2006)
Prediction of cancer outcome with microarrays: a multiple random validation strategy
S Michiels et al.
LANCET (2005)
Regularization and variable selection via the elastic net
H Zou et al.
JOURNAL OF THE ROYAL STATISTICAL SOCIETY SERIES B-STATISTICAL METHODOLOGY (2005)
Outcome signature genes in breast cancer: is there a unique set?
L Ein-Dor et al.
BIOINFORMATICS (2005)
Gene expression profiling spares early breast cancer patients from adjuvant therapy: derived and validated in two population-based cohorts
Y Pawitan et al.
BREAST CANCER RESEARCH (2005)
Evolving gene/transcript definitions significantly alter the interpretation of GeneChip data
MH Dai et al.
NUCLEIC ACIDS RESEARCH (2005)
Exploration, normalization, and summaries of high density oligonucleotide array probe level data
RA Irizarry et al.
BIOSTATISTICS (2003)
Selection bias in gene extraction on the basis of microarray gene-expression data
C Ambroise et al.
PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA (2002)
Gene selection for cancer classification using support vector machines
I Guyon et al.
MACHINE LEARNING (2002)