From patterns to pathways: gene expression data analysis comes of age

By Donna K. Slonim. Nature Genetics, 2002

643 citations recorded by the source corpus.

Subfields: Aerospace Engineering, Astronomy and Astrophysics, Physiology

View Paper: https://www.semanticscholar.org/paper/2acf78dd4106f9fe0a1d025cc757d2addf24fb9a

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Citations in the corpus

A framework to identify physiological responses in microarray-based gene expression studies: selection and interpretation of biologically relevant genes. (2008, background): “In biological responses, gene-gene interactions will take place because these responses often result from coregulation of genes (4, 40).”
GENESIM: genetic extraction of a single, interpretable model (2016, background): “One of the disadvantages of decision trees is that they are prone to overﬁt [1].”
Computational cluster validation in post-genomic data analysis (2005, background): “In particular, clustering plays a crucial role in the analysis of gene-expression data [20, 28, 59, 65].”
Genome-wide midrange transcription profiles reveal expression level relationships in human tissue specification (2005, background): “Studies in model organisms such as Drosophila have demonstrated the complex interplay of signaling molecules that underlie developmental events associated with the embryonic maturation of tissues and cell types (St Johnston, 2002).”
Support vector machines : theory and applications (2005, background): “Microarrays [15, 16] are also called gene chips or DNA chips.”
The role of endomyocardial biopsy in the management of cardiovascular disease: a scientific statement from the American Heart Association, the American College of Cardiology, and the European Society of Cardiology. (2007, background): “Various studies have identified differentially expressed genes (155) and clustering gene expression profiles to find functional groupings of genes (156).”
The properties of high-dimensional data spaces: implications for exploring gene and protein expression data (2008, background): “By contrast, studies in which genes or proteins are treated as observations and samples or patients are treated as variables, such as in gene function predictio”
Clustering cancer gene expression data: a comparative study (2008, methodology): “In terms of the data sets, it is important to point out that microarray technology is generally available in two different types of platforms, single-channel microarrays (e.g., Affymetrix) or double-channel microarrays (e.g., cDNA) [46,47,49].”
A multiple kernel support vector machine scheme for feature selection and rule extraction from gene expression data of cancer tissue (2007, background): “The cancer is fundamentally themalfunction of genes, so utilizing the gene expression data might be themost direct diagnosisway [1—5].”
FatiGO: a web tool for finding significant associations of Gene Ontology terms with groups of genes (2004, background): “Multiple testing is an important issue that is, nevertheless, scarcely addressed (Slonim, 2002).”
Obesity is associated with macrophage accumulation in adipose tissue. (2003, methodology): “We used a false discovery rate rather than standard significance measures to avoid an inflated false-positive rate as a consequence of the large number of hypothesis tests (42).”