Results 31 to 40 of about 15,041 (261)
Sparse Canonical Correlation Analysis for Multiple Measurements With Latent Trajectories. [PDF]
Biom JABSTRACT Canonical correlation analysis (CCA) is a widely used multivariate method in omics research for integrating high‐dimensional datasets. CCA identifies hidden links by deriving linear projections of observed features that maximally correlate datasets. An important requirement of standard CCA is that observations are independent
Senar N, Zwinderman AH, Hof MH.
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Identifying diagnosis-specific genotype-phenotype associations via joint multitask sparse canonical correlation analysis and classification. [PDF]
Bioinformatics, 2020 Motivation Brain imaging genetics studies the complex associations between genotypic data such as single nucleotide polymorphisms (SNPs) and imaging quantitative traits (QTs).
Du L +9 more
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Sparse Canonical Correlation Analysis (SCCA): A Comparative Study [PDF]
, 2012 Canonical Correlation Analysis (CCA) is one of the multivariate statistical methods that can be used to find relationship between two sets of variables. I highlighted challenges in analyzing high-dimensional data with CCA. Recently, Sparse CCA (SCCA) methods have been proposed to identify sparse linear combinations of two sets of variables with maximal
Pichika, Sathish chandra
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Sparse canonical correlation analysis [PDF]
Machine Learning, 2010 We present a novel method for solving Canonical Correlation Analysis (CCA) in a sparse convex framework using a least squares approach. The presented method focuses on the scenario when one is interested in (or limited to) a primal representation for the first view while having a dual representation for the second view.
David R. Hardoon, John Shawe-Taylor
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IEEE Access, 2021 Imaging genetics research based on Sparse Canonical Correlation Analysis (SCCA) helps to discover the correlation between pathological features reflected by neuroimaging and genotypic variation.
Kai Wei, Wei Kong, Shuaiqun Wang
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BMC Bioinformatics, 2022 Background With the development of noninvasive imaging technology, collecting different imaging measurements of the same brain has become more and more easy.
Jin Zhang +5 more
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Comparison of Penalty Functions for Sparse Canonical Correlation Analysis. [PDF]
Comput Stat Data Anal, 2012 Canonical correlation analysis (CCA) is a widely used multivariate method for assessing the association between two sets of variables. However, when the number of variables far exceeds the number of subjects, such in the case of large-scale genomic studies, the traditional CCA method is not appropriate.
Chalise P, Fridley BL.
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Integrating multi-OMICS data through sparse canonical correlation analysis for the prediction of complex traits: a comparison study. [PDF]
Bioinformatics, 2020 Motivation Recent developments in technology have enabled researchers to collect multiple OMICS datasets for the same individuals. The conventional approach for understanding the relationships between the collected datasets and the complex trait of ...
Rodosthenous T +2 more
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Microorganisms, 2020 Gut microbiota and the host exist in a mutualistic relationship, with the functional composition of the microbiota strongly influencing the health and well-being of the host.
Kejun He +5 more
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Frontiers in Neuroscience, 2022 Recent studies have proved that dynamic regional measures extracted from the resting-state functional magnetic resonance imaging, such as the dynamic fractional amplitude of low-frequency fluctuation (d-fALFF), could provide a great insight into brain ...
Peilun Song +7 more
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