Independent component analysis is a well-known tool for extracting underlying mechanisms from an observed set of parallel data. Identifying such components ...
This paper illustrates how to utilize independent component analysis on cell line data for achieving this goal.
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Independent component analysis (ICA) [17] is a statistical and computational technique designed to reveal hidden factors that underlie sets of random variables, ...
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In this study, we apply independent component analysis (ICA) to human breast cancer proteogenomics data to retrieve mechanistic information.
We show that ICA outperforms PCA and clustering-based methods in that ICA components map closer to known cancer-related pathways, regulatory modules, and cancer ...
Independent Component Analysis for Unraveling the Complexity of ...
pmc.ncbi.nlm.nih.gov › PMC6771121
Sep 7, 2019 · Independent component analysis (ICA) is a standard tool for reducing the complexity of omics datasets in cancer biology. (a) ICA belongs to the ...
Sep 9, 2020 · We proposed a method of attribute selection and feature extraction based on random forest (RF) combined with principal component analysis (PCA) for rapid and ...
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As a technique of higher-order statistical analysis, ICA is capable of extracting biologically relevant gene expression features from microarray data.
Oct 22, 2024 · Independent Component Analysis (ICA) is one of a few number of unsupervised algorithms that have been applied to microarray gene expression data ...
Breast cancer risk assessment: Principal Component Analysis (PCA) was used to conduct a two-level feature extraction for the purpose of identifying the factors ...
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