Generalized singular value decomposition for comparative analysis of genome-scale expression data sets of two different organisms

By O. Alter, Patrick O. Brown, David Botstein. Proceedings of the National Academy of Sciences of the United States of America, 2003

330 citations recorded by the source corpus.

Subfields: Molecular Biology

View Paper: https://www.semanticscholar.org/paper/3cbe25f4c796f7b3b72bddbfc95599944d2cd3e4

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In Molecular Biology

Citations in the corpus

All-at-once Optimization for Coupled Matrix and Tensor Factorizations (2011, background): “, population differentiation in biology [31], blind source separation [37], multimicrophone speech filtering [11], and microarray data analysis [4, 5, 6].”
Analyzing time series gene expression data (2004, methodology): “…Gasch et al., 2000), in which clusters of genes are compared across the two experiments and generalized singular value decomposition (SVD) [presented by Alter et al. (2003)], which are also used to detect differences between sets of genes, but are less appropriate for comparing individual genes.”
From big data analysis to personalized medicine for all: challenges and opportunities (2015, background): “Other similar, but conceptually different approaches include generalized singular value decomposition [91], and integrative bioclustering methods [92, 93].”
PAC-Bayesian Analysis of Co-clustering and Beyond (2010, neutral): “For example, Alter et al. (2003) applied generalized SVD (GSVD) to compare yeast and human cell-cycle gene expression datasets.”
Multimodal Data Fusion: An Overview of Methods, Challenges, and Prospects (2015, background): “Ponna-palli et al. [171] extend this GSVD-based approach to more than two datasets.”
Data integration in the era of omics: current and future challenges (2014, background): “…these definitions, and considering exploitation of datasets with heterogeneous data types involved (either across studies or within studies) then tools such as Co-Inertia Analysis [30,31], Generalized Singular Value Decomposition [32] and Integrative Bi-Clustering [33] among others are relevant.”
Gene co-expression analysis for functional classification and gene–disease predictions (2017, methodology): “HO-GSVD recently proved effective at identifying pathways important for self-renewal of neural progenitors [136].”