Mining Outcome-relevant Brain Imaging Genetic Associations via Three-way Sparse Canonical Correlation Analysis in Alzheimer's Disease. [PDF]
Sci Rep, 2017 Neuroimaging genetics is an emerging field that aims to identify the associations between genetic variants (e.g., single nucleotide polymorphisms (SNPs)) and quantitative traits (QTs) such as brain imaging phenotypes.
Hao X +9 more
europepmc +4 more sources
Probabilistic canonical correlation analysis for sparse count data
Journal of Statistical Research, 2023 Canonical correlation analysis (CCA) is a classical and important multivariate technique for exploring the relationship between two sets of continuous variables. CCA has applications in many fields, such as genomics and neuroimaging. It can extract meaningful features as well as use these features for subsequent analysis.
Qiu, Lin, Chinchilli, Vernon M.
openaire +2 more sources
SuMO-Fil: Supervised multi-omic filtering prior to performing network analysis.
PLoS ONE, 2021 Multi-omic analyses that integrate many high-dimensional datasets often present significant deficiencies in statistical power and require time consuming computations to execute the analytical methods.
Lorin M Towle-Miller +3 more
doaj +1 more source
Sparse canonical correlation analysis relates network-level atrophy to multivariate cognitive measures in a neurodegenerative population [PDF]
NeuroImage, 2014 Brian B Avants +2 more
exaly +2 more sources
Spatial Error Concealment Based on Coupled Sparse Optimization [PDF]
Jisuanji gongcheng, 2019 Linear interpolation algorithm or conventional sparse representation algorithm are used to recover the lost pixels currently.However,linear interpolation restores image blur due to inconsistent neighborhood information when restoring unsmooth images.For ...
YAN Jingwen,XIAO Jing,GAO Ge
doaj +1 more source
Biclustering by sparse canonical correlation analysis
Quantitative Biology, 2018 BackgroundDeveloping appropriate computational tools to distill biological insights from large‐scale gene expression data has been an important part of systems biology. Considering that gene relationships may change or only exist in a subset of collected samples, biclustering that involves clustering both genes and samples has become in‐creasingly ...
Harold Pimentel, Zhiyue Hu, Haiyan Huang
openaire +1 more source
Sparse canonical methods for biological data integration: application to a cross-platform study
BMC Bioinformatics, 2009 Background In the context of systems biology, few sparse approaches have been proposed so far to integrate several data sets. It is however an important and fundamental issue that will be widely encountered in post genomic studies, when simultaneously ...
Robert-Granié Christèle +3 more
doaj +1 more source
Integrated micro/messenger RNA regulatory networks in essential thrombocytosis. [PDF]
PLoS ONE, 2018 Essential thrombocytosis (ET) is a chronic myeloproliferative disorder with an unregulated surplus of platelets. Complications of ET include stroke, heart attack, and formation of blood clots. Although platelet-enhancing mutations have been identified in
Lu Zhao +5 more
doaj +1 more source
Sparse semiparametric canonical correlation analysis for data of mixed types [PDF]
Biometrika, 2020 SummaryCanonical correlation analysis investigates linear relationships between two sets of variables, but it often works poorly on modern datasets because of high dimensionality and mixed data types such as continuous, binary and zero-inflated. To overcome these challenges, we propose a semiparametric approach to sparse canonical correlation analysis ...
Yoon, Grace +2 more
openaire +3 more sources
Group sparse canonical correlation analysis for genomic data integration. [PDF]
BMC Bioinformatics, 2013 Abstract Background The emergence of high-throughput genomic datasets from different sources and platforms (e.g., gene expression, single nucleotide polymorphisms (SNP), and copy number variation (CNV)) has greatly enhanced our understandings of the interplay of these genomic factors as well as their influences on the
Lin D +5 more
europepmc +4 more sources