INCORPORATING BIOLOGICAL INFORMATION INTO LINEAR MODELS: A BAYESIAN APPROACH TO THE SELECTION OF PATHWAYS AND GENES.
about
BayesHammer: Bayesian clustering for error correction in single-cell sequencingStatistical Methods in Integrative GenomicsJoint network and node selection for pathway-based genomic data analysis.Analyzing LC/MS metabolic profiling data in the context of existing metabolic networks.A spatio-temporal nonparametric Bayesian variable selection model of fMRI data for clustering correlated time courses.A non-negative matrix factorization method for detecting modules in heterogeneous omics multi-modal data.A Bayesian integrative approach for multi-platform genomic data: A kidney cancer case study.Characterization of biological pathways associated with a 1.37 Mbp genomic region protective of hypertension in Dahl S rats.Molecular pathway identification using biological network-regularized logistic modelsVariable selection for discriminant analysis with Markov random field priors for the analysis of microarray dataIntegrative variable selection via Bayesian model uncertaintyOn differential gene expression using RNA-Seq dataBayesian joint selection of genes and pathways: applications in multiple myeloma genomicsComputational methods and opportunities for phosphorylation network medicineIntegrative Bayesian variable selection with gene-based informative priors for genome-wide association studies.Bayesian detection of causal rare variants under posterior consistencyA Bayesian Group Sparse Multi-Task Regression Model for Imaging Genetics.Joint Bayesian variable and graph selection for regression models with network-structured predictorsAn integrative framework for Bayesian variable selection with informative priors for identifying genes and pathways.Increased proliferative cells in the medullary thick ascending limb of the loop of Henle in the Dahl salt-sensitive ratBayesian semiparametric regression models for evaluating pathway effects on continuous and binary clinical outcomes.A Bayesian extension of the hypergeometric test for functional enrichment analysisA bayesian integrative model for genetical genomics with spatially informed variable selection.A model-based analysis to infer the functional content of a gene list.Hierarchical Feature Selection Incorporating Known and Novel Biological Information: Identifying Genomic Features Related to Prostate Cancer Recurrence.Multivariate Bayesian variable selection exploiting dependence structure among outcomes: Application to air pollution effects on DNA methylation.Bayesian Variable Selection in Multilevel Item Response Theory Models with Application in Genomics.A BAYESIAN NONPARAMETRIC MIXTURE MODEL FOR SELECTING GENES AND GENE SUBNETWORKS.miRNA-target gene regulatory networks: A Bayesian integrative approach to biomarker selection with application to kidney cancer.An Integrative Bayesian Modeling Approach to Imaging Genetics.A Bayesian approach to identify genes and gene-level SNP aggregates in a genetic analysis of cancer data.Inclusion of biological knowledge in a Bayesian shrinkage model for joint estimation of SNP effects.Network-guided regression for detecting associations between DNA methylation and gene expression.Statistical Contributions to Bioinformatics: Design, Modeling, Structure Learning, and Integration.Bayesian Variable Selection Methods for Matched Case-Control Studies.Bayesian Multiresolution Variable Selection for Ultra-High Dimensional Neuroimaging Data.Transcriptomic analysis reveals inflammatory and metabolic pathways which are regulated by renal perfusion pressure in the outer medulla of Dahl-S rats.
P2860
Q27496610-316A3B38-09DF-4D1A-8E80-FBF12204A043Q28385219-7DBF7F87-9F0A-45D1-A8E1-8EF6381FE6D4Q30643757-1986E6E3-974A-4B44-A946-5B791525E31CQ30665314-5B21CEA9-EC0A-4CB7-A0CE-4057A5439313Q30781656-0CB76F57-3029-47F9-8A3E-4A7B5507B850Q30993887-EEC05165-CDC4-4005-9617-F99D9863C4B9Q31133163-8ED80BD3-1986-4A5D-9A01-1FAB5CA7A662Q33701868-46FBD251-D59C-46E0-AC6B-E6AD6315DE96Q33715204-F71445BC-70F9-468F-894B-0CBB9669D8AFQ33772442-3E275747-CBF3-4659-A76F-EFF1CA718556Q33858485-D49800E0-3586-4D19-8C0B-1902059FABA7Q34001523-71C508CA-6B17-49BD-B431-7BB6FF113D24Q34662428-61C9BB0A-5195-4D98-8343-506D9ADF0922Q34742859-A6B5DD65-372C-4B36-A458-7B9EC4B6FD83Q34768997-69BCAD94-EC7C-4DDC-8248-1B5B44687C06Q34903370-A9E8B5BC-E665-40A1-893D-622FCDF16BF0Q36349847-6103BE8E-A9E8-4E9B-9B83-46F08FEE380AQ36642833-EB362FFC-4FD5-4104-BDD1-7D0144844B91Q36981115-4B6FA6B6-C1FF-4E97-A8E2-7D8863CC0A11Q37141433-E4D22CBC-566D-4145-951A-7C1EE2B5AE76Q37149352-6EAC5497-BCA3-4040-B3D0-1215C0FADE0CQ38273411-D25CC763-CE0D-41D0-A535-A2A4A9B8978CQ38471824-DD2834EE-C8CF-4DA0-AF16-E7154B9B72FCQ38497608-F265AC61-913F-404A-B576-06989AFE5718Q38824071-9D9BEDAF-2853-49B8-B691-E4DF416907ABQ39632475-5F0C77E0-BDE1-4ADC-8868-635440F5A5C4Q39878449-871B6C99-BEDA-4152-AFAC-E315BCD6F89AQ40710098-AC57FAF9-C3F2-4653-A59C-D4EC8FE58D67Q41114612-E88BE47F-3882-4235-A05E-D3D56B1606F6Q42012953-3A895D17-6FC8-491E-8A53-206BD05460DAQ42063470-0D5995E5-B842-4AA8-8FA4-88C4B2923A75Q45958612-5E60BAFC-A010-4934-B874-482ED5142AE8Q46132991-D43AAA19-0E7C-474E-9760-90996EAD24C5Q47110630-5E1D18F7-7A73-4BE8-9B3C-EFF33F5690B3Q52106596-B68780B0-3FE6-4D48-B3C1-95A80D8E4BE1Q52612075-F7436F91-88E6-4980-8F04-6BB67525958BQ52618447-70AA1962-F206-4254-87A7-D71561D91016
P2860
INCORPORATING BIOLOGICAL INFORMATION INTO LINEAR MODELS: A BAYESIAN APPROACH TO THE SELECTION OF PATHWAYS AND GENES.
description
2011 nî lūn-bûn
@nan
2011年の論文
@ja
2011年論文
@yue
2011年論文
@zh-hant
2011年論文
@zh-hk
2011年論文
@zh-mo
2011年論文
@zh-tw
2011年论文
@wuu
2011年论文
@zh
2011年论文
@zh-cn
name
INCORPORATING BIOLOGICAL INFOR ...... LECTION OF PATHWAYS AND GENES.
@en
type
label
INCORPORATING BIOLOGICAL INFOR ...... LECTION OF PATHWAYS AND GENES.
@en
prefLabel
INCORPORATING BIOLOGICAL INFOR ...... LECTION OF PATHWAYS AND GENES.
@en
P2093
P2860
P356
P1476
INCORPORATING BIOLOGICAL INFOR ...... LECTION OF PATHWAYS AND GENES.
@en
P2093
Francesco C Stingo
Mahlet G Tadesse
Yian A Chen
P2860
P304
P356
10.1214/11-AOAS463
P577
2011-09-01T00:00:00Z