Linear modes of gene expression determined by independent component analysis

By Wolfram Liebermeister. Bioinform., 2002

366 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/3e7aa9b43ac91cdb4f2cddee13566d25b4717224

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Molecular Biology

Citations in the corpus

Blind Source Separation and Independent Component Analysis: A Review (2005, methodology): “Spatial ICA or spatio-temporal ICA were shown to be useful in fMRI image analysis [133] and gene expression data analysis [103, 86].”
Generalized Power Method for Sparse Principal Component Analysis (2008, methodology): “Further methods for dimension reduction, such as independent component analysis (Liebermeister [13]) or nonnegative matrix factorization (Brunet et al.”
Singular value decomposition and principal component analysis (2003, methodology): “Liebermeister has applied ICA to gene expression data (Liebermeister, 2002).”
Numerical construction of Green's functions in high dimensional elliptic problems with variable coefficients and analysis of renewable energy data via sparse and separable approximations (2012, background): “Depending on the application, it may be beneficial to instead incorporate specific knowledge directly into whichever algorithm is being used, be it non-negativity of the spectral matrices [113, 92], statistical independence of the underlying factors [79, 93], or sparsity of the data [132, 75].”
Learning a hierarchical representation of the yeast transcriptomic machinery using an autoencoder model (2016, methodology): “Different latent variable models, such as principle component analysis [1], independent component analysis [2], Bayesian vector quantizer model [3], network component analysis [4–6], and non-negative matrix factorization [5, 6] models have been applied to analyze transcriptomic data, with an aim to ”
Blind Source Separation and Independent Component Analysis : A Review (2004, methodology): “Spatial ICA or spatio-temporal ICA were shown to be useful in fMRI image analysis [133] and gene expression data analysis [103, 86].”
Icasso: software for investigating the reliability of ICA estimates by clustering and visualization (2003, background): “In general, Icasso consists of the following steps: 1.”