Discovering functional relationships between RNA expression and chemotherapeutic susceptibility using relevance networks.

By Atul J. Butte, Pablo Tamayo, Donna K. Slonim, Todd R. Golub, Isaac S. Kohane. Proceedings of the National Academy of Sciences of the United States of America, 2000

643 citations recorded by the source corpus.

Subfields: Molecular Biology

View Paper: https://www.semanticscholar.org/paper/0b092bac8095dba6067f4994d9f218043fcd3237

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Citations in the corpus

Wishart distributions for decomposable covariance graph models (2011, background): “[3], Grzebyk, Wild and Chouaniere [15], Mao, Kschischang and Frey [21] and others.”
Sparse canonical methods for biological data integration: application to a cross-platform study (2009, methodology): “0 Dimension 1 D im en si on 2 V00594_at Z24727_at M23254_at M19267_s_at Z18951_atX53416_at X64177_f_at D42073_at D29963_at V00 94_s_at X86809_at D00017_at X07979_at M333 8_at X05610 t L25081_at U08021_at U89942_at M31013_at U62015_at U51010_s_at S79639_at U22376_cds2_s_at J03764_at M26576_cds2_at M3”
From patterns to pathways: gene expression data analysis comes of age (2002, methodology): “Integrating clinical data from patients’ records has been proposed as an approach to aiding interpretation 87,88 , and preliminary approaches suggest ways of combining microarray data with other clinical or experimental variables 89,90 .”
TIGRESS: Trustful Inference of Gene REgulation using Stability Selection (2012, methodology): “For example, the correlation or mutual information between the expression levels of t and g along the different experiments is a popular way to score candidate regulations [12-14].”
Statistical Applications in Genetics and Molecular Biology A Shrinkage Approach to Large-Scale Covariance Matrix Estimation and Implications for Functional Genomics (2011, positive): “Another example is the construction of so-called relevance networks (Butte et al., 2000).”
Information-Theoretic Inference of Large Transcriptional Regulatory Networks (2007, methodology): “The relevance network approach [4] has been introduced in gene clustering problems and successfully applied to infer relationships between RNA expression and chemotherapeutic susceptibility [15].”
Towards a Rigorous Assessment of Systems Biology Models: The DREAM3 Challenges (2010, result): “…be applied off-the-shelf, a strategy for guarding against bogus predictions is to employ a wide variety of algorithms that train on different features of the data (e.g., correlation [28], mutual information [29–32], synergy [33], etc.), then merge the results to gain confidence in the predictions.”
Gene co-expression network topology provides a framework for molecular characterization of cellular state (2004, methodology): “‘Relevance networks’ is an unsupervised technique for generating networks of expression profile associations based on exhaustive pairwise comparison coupled with permutationbased significance thresholding (Butte et al., 2000).”
Analysis of matched mRNA measurements from two different microarray technologies (2002, background): “Details about the publicly available gene expression data sets have been described previously (Butte et al., 2000; Ross et al., 2000).”
Statistical Estimation of Correlated Genome Associations to a Quantitative Trait Network (2009, methodology): “This thresholded correlation graph is also known as a relevance network, and has been widely used as a representation of gene interaction networks [ 29 ,30].”
A kernel method for unsupervised structured network inference (2009, methodology): “We compare to three unsupervised network inference methods: correlation relevance networks (Butte et al., 2000), mutual information relevance networks (Butte & Kohane, 2000), and graphical Gaussian models (Toh & Horimoto, 2002).”
Large-Scale Mapping and Validation of Escherichia coli Transcriptional Regulation from a Compendium of Expression Profiles (2007, methodology): “These methods were relevance networks [29], ARACNe [2], and Bayesian networks [7].”
Comparison of co-expression measures: mutual information, correlation, and model based indices (2012, methodology): “RELNET The relevance network (RELNET) approach [6,28] thresholds the pairwise measures of mutual information by a threshold τ .”
minet: A R/Bioconductor Package for Inferring Large Transcriptional Networks Using Mutual Information (2008, methodology): “The relevance network approach [7] has been introduced in gene clustering and was successfully applied to infer relationships between RNA expressions and chemotherapeutic susceptibility [6].”
Geometric Interpretation of Gene Coexpression Network Analysis (2008, methodology): “. Gene coexpression networks have been used to describe the transcriptome in many organisms, e.g., yeast, flies, worms, plants, mice, and humans”