A Markov random field model for network-based analysis of genomic data.
about
Simulation-based model selection for dynamical systems in systems and population biology.Identifying cell types from spatially referenced single-cell expression datasetsNetwork-based classification of breast cancer metastasisProtein side-chain resonance assignment and NOE assignment using RDC-defined backbones without TOCSY dataA hierarchical semiparametric model for incorporating intergene information for analysis of genomic data.Network-based analysis of multivariate gene expression data.Joint network and node selection for pathway-based genomic data analysis.Differential gene expression analysis using coexpression and RNA-Seq data.Analyzing LC/MS metabolic profiling data in the context of existing metabolic networks.Hierarchy of gene expression data is predictive of future breast cancer outcomeBADGE: a novel Bayesian model for accurate abundance quantification and differential analysis of RNA-Seq data.A comparative study of improvements Pre-filter methods bring on feature selection using microarray dataMetTailor: dynamic block summary and intensity normalization for robust analysis of mass spectrometry data in metabolomics.mapDIA: Preprocessing and statistical analysis of quantitative proteomics data from data independent acquisition mass spectrometryA MARKOV RANDOM FIELD-BASED APPROACH TO CHARACTERIZING HUMAN BRAIN DEVELOPMENT USING SPATIAL-TEMPORAL TRANSCRIPTOME DATA.Integrative Analysis of Multi-omics Data for Discovery and Functional Studies of Complex Human Diseases.A Markov random field-based approach for joint estimation of differentially expressed genes in mouse transcriptome data.Network-enabled gene expression analysis.Transcriptomic characterization of differential gene expression in oral squamous cell carcinoma: a meta-analysis of publicly available microarray data sets.Incorporating gene networks into statistical tests for genomic data via a spatially correlated mixture model.Network-constrained regularization and variable selection for analysis of genomic data.Powerful differential expression analysis incorporating network topology for next-generation sequencing data.Testing gene set enrichment for subset of genes: Sub-GSE.Incorporating predictor network in penalized regression with application to microarray data.Identifying differentially methylated genes using mixed effect and generalized least square models.A Bayesian approach to joint modeling of protein-DNA binding, gene expression and sequence data.Bayesian Markov Random Field analysis for protein function prediction based on network dataPathway-BasedFeature Selection Algorithm for Cancer Microarray Data.Network-based empirical Bayes methods for linear models with applications to genomic data.Using pre-existing microarray datasets to increase experimental power: application to insulin resistance.A hidden Markov random field model for genome-wide association studies.Pathway analysis using random forests with bivariate node-split for survival outcomesStatistical methods for integrating multiple types of high-throughput dataMicroRNA-integrated and network-embedded gene selection with diffusion distanceVariable selection for discriminant analysis with Markov random field priors for the analysis of microarray dataKnockdown of P4HA1 inhibits neovascularization via targeting glioma stem cell-endothelial cell transdifferentiation and disrupting vascular basement membraneAssessing 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 studiesThe common ground of genomics and systems biology.Identifying cancer biomarkers by network-constrained support vector machines.
P2860
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P2860
A Markov random field model for network-based analysis of genomic data.
description
2007 nî lūn-bûn
@nan
2007 թուականի Մայիսին հրատարակուած գիտական յօդուած
@hyw
2007 թվականի մայիսին 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
A Markov random field model for network-based analysis of genomic data.
@ast
A Markov random field model for network-based analysis of genomic data.
@en
type
label
A Markov random field model for network-based analysis of genomic data.
@ast
A Markov random field model for network-based analysis of genomic data.
@en
prefLabel
A Markov random field model for network-based analysis of genomic data.
@ast
A Markov random field model for network-based analysis of genomic data.
@en
P2860
P356
P1433
P1476
A Markov random field model for network-based analysis of genomic data.
@en
P2093
P2860
P304
P356
10.1093/BIOINFORMATICS/BTM129
P407
P577
2007-05-05T00:00:00Z