Transitive functional annotation by shortest-path analysis of gene expression data

By X. Zhou, M. Kao, Wing Hung Wong. Proceedings of the National Academy of Sciences of the United States of America, 2002

359 citations recorded by the source corpus.

Subfields: Molecular Biology

View Paper: https://www.semanticscholar.org/paper/3060758f4282338fd10eb319ce968c81036a0465

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In Molecular Biology

Citations in the corpus

Significance analysis of functional categories in gene expression studies: a structured permutation approach (2005, background): “These patterns can in turn be used to annotate genes and predict function in a probabilistic manner (Zhou et al., 2002; Troyanskaya et al., 2003).”
Gene network interconnectedness and the generalized topological overlap measure (2007, methodology): “Such a setting has also been used in [26] for functional annotation.”
Eigengene networks for studying the relationships between co-expression modules (2007, background): “Background Gene co-expression networks constructed from gene expression microarray data capture the relationships between transcripts [1-7].”
WGCNA: an R package for weighted correlation network analysis (2008, background): “Co-expression networks have been found useful for describing the pairwise relationships among gene transcripts [2-9].”
Sparse graphical models for exploring gene expression data (2004, neutral): “Our outlook is similar to that of [30] where direct, correlation based analysis was able to define biologically interpretable connections transitively across groups of genes.”
Predicting protein function from protein/protein interaction data: a probabilistic approach (2003, background): “…across organisms (Gaasterland and Ragan, 1998; Pellegrini et al., 1999), and analysis of measurements of gene expression to identify genes that have similar expression patterns, which provides evidence of co-regulation and hence possible shared function (Ulanovsky et al., 2002; Zhou et al., 2002).”
Molecular ecological network analyses (2012, background): “However, the former possibility can be complicated by the observations that functionally redundant genes are not necessary co-regulated, but instead co-regulated through other genes, which is coined as transitive co-regulation [78].”
Computational solutions for omics data (2013, methodology): “In contrast to principle component analysis (PCA) 92,93 or correlation-based methods 93,94 , SPARCLE is able to find robust gene sets of much smaller size from high-dimensional transcriptomes (that is, transcriptomes in which a large number of gene expression changes are observed), such that they…”
Penalized Model-Based Clustering with Application to Variable Selection (2007, background): “Clustering gene expression profiles has become a popular approach for both gene function prediction and discovery (Eisen et al., 1998; Wu et al., 2002; Zhou et al., 2002; Xiao and Pan, 2005).”
Local Causal and Markov Blanket Induction for Causal Discovery and Feature Selection for Classification Part I: Algorithms and Empirical Evaluation (2010, methodology): “Whole research programs are, in many domains, built on experiments motivated by causal hypotheses that were generated by non-causal feature selection results (Zhou et al., 2002; Li et al., 2001; Holmes et al., 2000; Eisen et al., 1998) and this seems an unfortunate and inadvisable practice, in light”
Is My Network Module Preserved and Reproducible? (2011, background): “structed on the basis of gene expression data [3–10].”
Comparison of co-expression measures: mutual information, correlation, and model based indices (2012, background): “majority of published articles use the correlation coefficient as co-expression measure [1-5] but hundreds of articles have used the mutual information (MI) measure [6-12].”
Systematic noise degrades gene co-expression signals but can be corrected (2015, neutral): “[1]), gene annotation [2, 3], and even for assessing preservation of biological systems across different species [4].”