about
Incorporating network structure in integrative analysis of cancer prognosis data.Integrative Analysis of "-Omics" Data Using Penalty Functions.Promoting similarity of model sparsity structures in integrative analysis of cancer genetic data.Sparse group penalized integrative analysis of multiple cancer prognosis datasets.Differentially co-expressed genes in postmortem prefrontal cortex of individuals with alcohol use disorders: influence on alcohol metabolism-related pathways.Integrative analysis of prognosis data on multiple cancer subtypesAnalysis of genome-wide association studies with multiple outcomes using penalization.Similarity of markers identified from cancer gene expression studies: observations from GEOIntegrative analysis of high-throughput cancer studies with contrasted penalization.A penalized robust method for identifying gene-environment interactions.Effect of linkage disequilibrium on the identification of functional variants.IGESS: A Statistical Approach to Integrating Individual-Level Genotype Data and Summary Statistics in Genome-Wide Association Studies.Canonical variate regression.Identification of gene-environment interactions in cancer studies using penalization.The transcriptomic G1-G6 signature of hepatocellular carcinoma in an Asian population: Association of G3 with microvascular invasion.Analyzing Association Mapping in Pedigree-Based GWAS Using a Penalized Multitrait Mixed Model.EPS: an empirical Bayes approach to integrating pleiotropy and tissue-specific information for prioritizing risk genes.Incorporating group correlations in genome-wide association studies using smoothed group Lasso.Integrative Analysis of Cancer Diagnosis Studies with Composite PenalizationLLR: A latent low-rank approach to colocalizing genetic risk variants in multiple GWAS.Metabolomic profile of arterial stiffness in aged adults.LSMM: A statistical approach to integrating functional annotations with genome-wide association studies.LPG: A four-group probabilistic approach to leveraging pleiotropy in genome-wide association studies.CoMM: a collaborative mixed model to dissecting genetic contributions to complex traits by leveraging regulatory informationPrognostic value of CD8 + PD-1+ immune infiltrates and PDCD1 gene expression in triple negative breast cancerGenomic landscape of lung adenocarcinoma in East AsiansIGREX for quantifying the impact of genetically regulated expression on phenotypesBayesian weighted Mendelian randomization for causal inference based on summary statisticsCoMM: A Collaborative Mixed Model That Integrates GWAS and eQTL Data Sets to Investigate the Genetic Architecture of Complex TraitsLEP: A Statistical Method Integrating Individual-Level and Summary-Level Data of the Same Trait From Different PopulationsCoMM-S2: a collaborative mixed model using summary statistics in transcriptome-wide association studiesVIMCO: variational inference for multiple correlated outcomes in genome-wide association studiesMultiplex immunohistochemistry/immunofluorescence (mIHC/IF) for PD-L1 testing in triple-negative breast cancer: a translational assay compared with conventional IHC
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description
researcher ORCID ID=0000-0002-5707-2078
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name
Jin Liu
@ast
Jin Liu
@en
Jin Liu
@es
Jin Liu
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type
label
Jin Liu
@ast
Jin Liu
@en
Jin Liu
@es
Jin Liu
@nl
prefLabel
Jin Liu
@ast
Jin Liu
@en
Jin Liu
@es
Jin Liu
@nl
P108
P106
P1153
54786571400
P1960
V_yMDw0AAAAJ
P2456
01/2537-11
P31
P496
0000-0002-5707-2078