From signatures to models: understanding cancer using microarrays

By Eran Segal, Nir Friedman, Naftali Kaminski, Aviv Regev, Daphne Koller. Nature Genetics, 2005

410 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/543a0cff39eba2326ba6407292772ae33a84ebd6

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Citations in the corpus

Decoding global gene expression programs in liver cancer by noninvasive imaging (2007, background): “Gene expression patterns of cancer can reveal its etiology, prognosis and response to therap”
A modular analysis framework for blood genomics studies: application to systemic lupus erythematosus (2008, background): “Among those, approaches to uncover the modular organization and function of transcriptional systems have already shown promise (Mootha et al., 2003; Rhodes et al., 2005; Segal et al., 2004; reviewed in Segal et al., 2005).”
An embryonic stem cell–like gene expression signature in poorly differentiated aggressive human tumors (2008, background): “Detection of the activity of such complex networks must rely on expression analysis of many genes in multiple cancer samples, rather than on the presence or absence of individual factor”
Splicing in disease: disruption of the splicing code and the decoding machinery (2007, background): “Standard microarrays provided a robust signature for tumour identificatio”
Radiomics: the bridge between medical imaging and personalized medicine (2017, positive): “Gene-expression signatures, each comprised of dozens to hundreds of genes, can mean-ingfully improve diagnosis, prognosis, and prediction of response to treatment 47–52 .”
Inference of patient-specific pathway activities from multi-dimensional cancer genomics data using PARADIGM (2010, neutral): “Availability: Source code available at http://sbenz.github.com/Paradigm Contact: jstuart@soe.ucsc.edu Supplementary information: Supplementary data are available at Bioinformatics online.”
Inferring cellular networks – a review (2007, methodology): “Further reading There are a number of other reviews and method comparisons available: some focus on applications and biological interpretations [126-128], and some concentrate on methodology [129-137].”
Integrative analysis of the cancer transcriptome (2005, background): “In a related Perspective, Koller and colleagues discuss computational approaches for defining higher-level functional modules and regulatory models from transcriptome dat”
A framework to identify physiological responses in microarray-based gene expression studies: selection and interpretation of biologically relevant genes. (2008, background): “Although a large number of methods exist to select genes from microarray data sets, most methods aim to identify the smallest possible set of genes that still can discriminate, for example, to classify malignancies, predict therapeutic outcomes, or diagnose physiological responses (7, 38).”
Natural history and evolutionary principles of gene duplication in fungi (2007, methodology): “We constructed the hierarchy of transcriptional modules following the procedure presented by Segal et al.(37).”
Identification and validation of oncogenes in liver cancer using an integrative oncogenomic approach. (2006, background): “For example, gene-expression profiling using microarrays has improved the classification of some tumor types (Segal et al., 2005).”