Identification of gene pathways implicated in Alzheimer's disease using longitudinal imaging phenotypes with sparse regression.
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The ENIGMA Consortium: large-scale collaborative analyses of neuroimaging and genetic dataA review of multivariate analyses in imaging geneticsThe Alzheimer's Disease Neuroimaging Initiative: a review of papers published since its inceptionPlacental DNA Methylation Related to Both Infant Toenail Mercury and Adverse Neurobehavioral OutcomesGenetic analysis of quantitative phenotypes in AD and MCI: imaging, cognition and biomarkersThe influence of insulin resistance on cerebrospinal fluid and plasma biomarkers of Alzheimer's pathologyRegularized Machine Learning in the Genetic Prediction of Complex TraitsGroup sparse canonical correlation analysis for genomic data integration.Sparse models for correlative and integrative analysis of imaging and genetic data2014 Update of the Alzheimer's Disease Neuroimaging Initiative: A review of papers published since its inception.Bayesian longitudinal low-rank regression models for imaging genetic data from longitudinal studiesA novel relationship for schizophrenia, bipolar and major depressive disorder Part 5: a hint from chromosome 5 high density association screen.Gene expression profiling of brains from bovine spongiform encephalopathy (BSE)-infected cynomolgus macaques.Genetic modifiers of cognitive maintenance among older adultsIdentifying the neuroanatomical basis of cognitive impairment in Alzheimer's disease by correlation- and nonlinearity-aware sparse Bayesian learning.Longitudinal analysis is more powerful than cross-sectional analysis in detecting genetic association with neuroimaging phenotypes.Bayesian Generalized Low Rank Regression Models for Neuroimaging Phenotypes and Genetic Markers.Mitochondrial haplotypes associated with biomarkers for Alzheimer's disease.Genes from a translational analysis support a multifactorial nature of white matter hyperintensities.Pathways-driven sparse regression identifies pathways and genes associated with high-density lipoprotein cholesterol in two Asian cohortsRandom forests on Hadoop for genome-wide association studies of multivariate neuroimaging phenotypes.Reprint of: Mapping connectivity in the developing brain.Drug voyager: a computational platform for exploring unintended drug action.Possible relationship between common genetic variation and white matter development in a pilot study of preterm infantsPathways to neurodegeneration: mechanistic insights from GWAS in Alzheimer's disease, Parkinson's disease, and related disordersMapping connectivity in the developing brain.Brain insulin resistance deteriorates cognition by altering the topological features of brain networks.Altered protein phosphorylation as a resource for potential AD biomarkers.Characterizing Gene and Protein Crosstalks in Subjects at Risk of Developing Alzheimer’s Disease: A New Computational ApproachNeuroimaging genetic risk for Alzheimer's disease in preclinical individuals: From candidate genes to polygenic approaches
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P2860
Identification of gene pathways implicated in Alzheimer's disease using longitudinal imaging phenotypes with sparse regression.
description
2012 nî lūn-bûn
@nan
2012年の論文
@ja
2012年論文
@yue
2012年論文
@zh-hant
2012年論文
@zh-hk
2012年論文
@zh-mo
2012年論文
@zh-tw
2012年论文
@wuu
2012年论文
@zh
2012年论文
@zh-cn
name
Identification of gene pathway ...... otypes with sparse regression.
@ast
Identification of gene pathway ...... otypes with sparse regression.
@en
type
label
Identification of gene pathway ...... otypes with sparse regression.
@ast
Identification of gene pathway ...... otypes with sparse regression.
@en
prefLabel
Identification of gene pathway ...... otypes with sparse regression.
@ast
Identification of gene pathway ...... otypes with sparse regression.
@en
P2860
P50
P1433
P1476
Identification of gene pathway ...... otypes with sparse regression.
@en
P2093
Eva Janousova
Giovanni Montana
P2860
P304
P356
10.1016/J.NEUROIMAGE.2012.08.002
P407
P577
2012-08-15T00:00:00Z