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Independent component analysis (ICA) is a blind source separation method for linear disentanglement of independent latent sources from observed data.
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Gene expression data classification using consensus independent component analysis
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Canica: Model-based extraction of reproducible group-level ica patterns from fmri time series
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Condition-dependent transcriptome reveals high-level regulatory architecture in bacillus subtilis
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Data-driven human transcriptomic modules determined by independent component analysis
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Refining diffuse large b-cell lymphoma subgroups using integrated analysis of molecular profiles
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Partitioning subjects based on high-dimensional fmri data: comparison of several clustering methods and studying the influence of ica data reduction in big data
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Nonlinear ica using auxiliary variables and generalized contrastive learning
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Trivializations for gradient-based optimization on manifolds
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Deep canonical correlation analysis
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Independent vector analysis: Identification conditions and performance bounds
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An experimentally supported model of the bacillus subtilis global transcriptional regulatory network
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Group-level component analyses of eeg: validation and evaluation
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Building a science of individual differences from fmri
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M. Lezcano-Casado · 2019
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Deconvolution of transcriptomes and mirnomes by independent component analysis provides insights into biological processes and clinical outcomes of melanoma patients
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Group ica for identifying biomarkers in schizophrenia:‘adaptive’networks via spatially constrained ica show more sensitivity to group differences than spatio-temporal regression
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The escherichia coli transcriptome mostly consists of independently regulated modules
A. Sastry et al · 2019
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Transcriptional programs define intratumoral heterogeneity of ewing sarcoma at single-cell resolution
M. Aynaud et al · 2020
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Application of transcriptional gene modules to analysis of caenorhabditis elegans’ gene expression data
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Learning robust representations via multi-view information bottleneck
M. Federici et al · 2020
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Independent vector analysis for common subspace analysis: Application to multi-subject fmri data yields meaningful subgroups of schizophrenia
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Modeling shared responses in neuroimaging studies through multiview ica
H. Richard et al · 2020
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Granular transcriptomic signatures derived from independent component analysis of bulk nervous tissue for studying labile brain physiologies
Z. Rusan et al · 2020
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Independent component analysis of e. coli’s transcriptome reveals the cellular processes that respond to heterologous gene expression
J. Tan et al · 2020
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Contrastive multiview coding
Y. Tian et al · 2020
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Shared independent component analysis for multi-subject neuroimaging
H. Richard et al · 2021
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Independent component analysis recovers consistent regulatory signals from disparate datasets
A. Sastry et al · 2021
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Improving gene function predictions using independent transcriptional components
C. Urzúa-Traslaviña et al · 2021
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