about
A bayesian framework that integrates heterogeneous data for inferring gene regulatory networksReverse Engineering of Genome-wide Gene Regulatory Networks from Gene Expression DataDiscovering key regulatory mechanisms from single-factor and multi-factor regulations in glioblastoma utilizing multi-dimensional data.Knowledge-fused differential dependency network models for detecting significant rewiring in biological networksStepwise group sparse regression (SGSR): gene-set-based pharmacogenomic predictive models with stepwise selection of functional priors.Improving the sensitivity of sample clustering by leveraging gene co-expression networks in variable selection.Inference of radio-responsive gene regulatory networks using the graphical lasso algorithm.The characterization of microRNA-mediated gene regulation as impacted by both target site location and seed match type.Synonymous Codon Usage Bias in the Plastid Genome is Unrelated to Gene Structure and Shows Evolutionary Heterogeneity.Gene Network Reconstruction by Integration of Prior Biological Knowledge.Regularized logistic regression with network-based pairwise interaction for biomarker identification in breast cancer.Biology-Driven Gene-Gene Interaction Analysis of Age-Related Cataract in the eMERGE Network.Incorporating prior biological knowledge for network-based differential gene expression analysis using differentially weighted graphical LASSO.Integrating full spectrum of sequence features into predicting functional microRNA-mRNA interactions.Asymmetric latent semantic indexing for gene expression experiments visualization.Integrating network reconstruction with mechanistic modeling to predict cancer therapies.Incorporating prior information into differential network analysis using non-paranormal graphical models.Conditional screening for ultra-high dimensional covariates with survival outcomes.A Bayesian Framework to Improve MicroRNA Target Prediction by Incorporating External Information.F-MAP: A Bayesian approach to infer the gene regulatory network using external hints.Kernel differential subgraph reveals dynamic changes in biomolecular networks.MPLasso: Inferring microbial association networks using prior microbial knowledge.Predicting Functional MicroRNA-mRNA Interactions.Applications of Bayesian network models in predicting types of hematological malignancies.
P2860
Q30844584-1252D0D9-8468-438C-A13D-1E6B746C983FQ30943430-36C1F363-9FF9-4C59-B765-28A32D666754Q30975352-BA2DFB18-17C0-44F2-A035-C664BAB142EBQ34038359-9C06EA23-7C2F-465E-8739-2C1FC0BF9FD7Q34988221-436E11CE-4D4C-4953-A160-43F00BA98775Q35178787-017F8AEA-BEEA-4D8B-8F87-07D7056855A2Q35215912-3C3DEB04-F397-4446-A9BE-08B9C00058F1Q35265259-B199EB1E-53DA-43AA-9E95-088F52135C45Q35390345-C5CDD6F6-A2F0-48A6-ADD2-9E057931D2E4Q35773963-758A7F37-0804-49A2-BF20-2A60B7C57D2EQ35937992-1F8A7DD9-67FE-416C-9632-0FE925B18169Q35997343-99446EAB-06C3-41E9-9700-26CF0CDF5FE8Q36277294-8678887D-2564-4F92-A45C-1A953B6B0402Q36721089-EDC30DC3-471A-4608-ACF7-32984562DE6BQ38390038-AEA3D959-B727-48E0-970F-C6BA48F675CEQ38729295-A14454E5-3480-4225-A580-0EC1A4D0C7F4Q38838330-F4B3773B-DD9C-46EA-9F42-D589260BADF7Q39117166-DE417E1A-5D6E-4A41-B91F-76E2FEAC7774Q41752043-13A2FD69-4315-40A6-AC6A-95B57069B55CQ42362587-4FA76852-C52F-4377-BD57-C7659E19544DQ47215648-F81DBCA5-8AC6-43B9-BBB8-1CEB364A64E7Q47215897-A74C0B82-22B7-4BD3-9854-22B34BDDD2D2Q51036997-2E9C0884-22F2-475C-9340-462A9E5F1227Q55258713-96084FCC-C2B7-410C-856D-48FE94F20AF5
P2860
description
article científic
@ca
article scientifique
@fr
articolo scientifico
@it
artigo científico
@pt
bilimsel makale
@tr
scientific article published on 16 August 2013
@en
vedecký článok
@sk
vetenskaplig artikel
@sv
videnskabelig artikel
@da
vědecký článek
@cs
name
Incorporating prior knowledge into Gene Network Study.
@en
Incorporating prior knowledge into Gene Network Study.
@nl
type
label
Incorporating prior knowledge into Gene Network Study.
@en
Incorporating prior knowledge into Gene Network Study.
@nl
prefLabel
Incorporating prior knowledge into Gene Network Study.
@en
Incorporating prior knowledge into Gene Network Study.
@nl
P2093
P2860
P356
P1433
P1476
Incorporating prior knowledge into Gene Network Study.
@en
P2093
F Anthony San Lucas
Wenlong Xu
Zixing Wang
P2860
P304
P356
10.1093/BIOINFORMATICS/BTT443
P407
P577
2013-08-16T00:00:00Z