Incorporating gene networks into statistical tests for genomic data via a spatially correlated mixture model.
about
A hierarchical semiparametric model for incorporating intergene information for analysis of genomic data.Network-based analysis of multivariate gene expression data.A comparative study of improvements Pre-filter methods bring on feature selection using microarray dataNetwork-constrained regularization and variable selection for analysis of genomic data.A Bayesian approach to joint modeling of protein-DNA binding, gene expression and sequence data.Pathway-BasedFeature Selection Algorithm for Cancer Microarray Data.Network-based empirical Bayes methods for linear models with applications to genomic data.A hidden Markov random field model for genome-wide association studies.Statistical methods for integrating multiple types of high-throughput dataAssessing the biological significance of gene expression signatures and co-expression modules by studying their network propertiesIncorporating biological pathways via a Markov random field model in genome-wide association studiesModeling Three-Dimensional Chromosome Structures Using Gene Expression DataBayesian Joint Modeling of Multiple Gene Networks and Diverse Genomic Data to Identify Target Genes of a Transcription FactorIntegrative analyses for omics data: a Bayesian mixture model to assess the concordance of ChIP-chip and ChIP-seq measurements.An inferential framework for biological network hypothesis testsStepwise group sparse regression (SGSR): gene-set-based pharmacogenomic predictive models with stepwise selection of functional priors.Network-induced classification kernels for gene expression profile analysis.Incorporating prior biological knowledge for network-based differential gene expression analysis using differentially weighted graphical LASSO.Enhanced construction of gene regulatory networks using hub gene informationA significance test for graph-constrained estimationNetwork-based multiple locus linkage analysis of expression traitsChoosing the right path: enhancement of biologically relevant sets of genes or proteins using pathway structureIncorporating prior knowledge into Gene Network Study.Mining gene functional networks to improve mass-spectrometry-based protein identification.Functional genomics and networks: new approaches in the extraction of complex gene modules.Integrative analysis of multiple genomic variables using a hierarchical Bayesian model.Perturbation Detection Through Modeling of Gene Expression on a Latent Biological Pathway Network: A Bayesian hierarchical approach.NETWORK ASSISTED ANALYSIS TO REVEAL THE GENETIC BASIS OF AUTISM.F-MAP: A Bayesian approach to infer the gene regulatory network using external hints.Network-based genomic discovery: application and comparison of Markov random field modelsNetwork enrichment analysis in complex experiments.Analysis of gene sets based on the underlying regulatory network.Incorporating biological prior knowledge for Bayesian learning via maximal knowledge-driven information priors.Incorporating interaction networks into the determination of functionally related hit genes in genomic experiments with Markov random fields.Identifying functional modules using expression profiles and confidence-scored protein interactions.MMG: a probabilistic tool to identify submodules of metabolic pathways.
P2860
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P2860
Incorporating gene networks into statistical tests for genomic data via a spatially correlated mixture model.
description
2007 nî lūn-bûn
@nan
2007 թուականի Դեկտեմբերին հրատարակուած գիտական յօդուած
@hyw
2007 թվականի դեկտեմբերին հրատարակված գիտական հոդված
@hy
2007年の論文
@ja
2007年論文
@yue
2007年論文
@zh-hant
2007年論文
@zh-hk
2007年論文
@zh-mo
2007年論文
@zh-tw
2007年论文
@wuu
name
Incorporating gene networks in ...... ally correlated mixture model.
@ast
Incorporating gene networks in ...... ally correlated mixture model.
@en
type
label
Incorporating gene networks in ...... ally correlated mixture model.
@ast
Incorporating gene networks in ...... ally correlated mixture model.
@en
prefLabel
Incorporating gene networks in ...... ally correlated mixture model.
@ast
Incorporating gene networks in ...... ally correlated mixture model.
@en
P2860
P356
P1433
P1476
Incorporating gene networks in ...... ally correlated mixture model.
@en
P2093
P2860
P304
P356
10.1093/BIOINFORMATICS/BTM612
P407
P50
P577
2007-12-14T00:00:00Z