Transforming RNA-Seq data to improve the performance of prognostic gene signatures.
about
multiClust: An R-package for Identifying Biologically Relevant Clusters in Cancer Transcriptome ProfilesA refined genome-scale reconstruction of Chlamydomonas metabolism provides a platform for systems-level analysesExpression of lncRNAs in Low-Grade Gliomas and Glioblastoma Multiforme: An In Silico Analysis.A pan-cancer analysis of prognostic genes.Multilevel regularized regression for simultaneous taxa selection and network construction with metagenomic count data.Assessing the consistency of public human tissue RNA-seq data sets.Pathway analysis for RNA-Seq data using a score-based approach.Better prediction by use of co-data: adaptive group-regularized ridge regression.RNA-Seq Count Data Modelling by Grey Relational Analysis and Nonparametric Gaussian ProcessDifferential expression analysis for RNAseq using Poisson mixed models.The Wright stuff: reimagining path analysis reveals novel components of the sex determination hierarchy in Drosophila melanogaster.Transcriptome Sequencing (RNAseq) Enables Utilization of Formalin-Fixed, Paraffin-Embedded Biopsies with Clear Cell Renal Cell Carcinoma for Exploration of Disease Biology and Biomarker Development.Expression of the MHC Class II Pathway in Triple-Negative Breast Cancer Tumor Cells Is Associated with a Good Prognosis and Infiltrating LymphocytesDealing with prognostic signature instability: a strategy illustrated for cardiovascular events in patients with end-stage renal diseaseIntegrating multiple molecular sources into a clinical risk prediction signature by extracting complementary information.Hierarchical probabilistic models for multiple gene/variant associations based on next-generation sequencing data.Profiling cancer testis antigens in non-small-cell lung cancer.Integrated genomic and molecular characterization of cervical cancer.Screening the Molecular Framework Underlying Local Dendritic mRNA Translation.Maize network analysis revealed gene modules involved in development, nutrients utilization, metabolism, and stress response.Maternal exposure to selenium and cadmium, fetal growth, and placental expression of steroidogenic and apoptotic genes.Integrated analysis of gene network in childhood leukemia from microarray and pathway databases.Estimation of cis-eQTL effect sizes using a log of linear model.Improved high-dimensional prediction with Random Forests by the use of co-data.Integrated analysis of DNA-methylation and gene expression using high-dimensional penalized regression: a cohort study on bone mineral density in postmenopausal women.Assessment of data transformations for model-based clustering of RNA-Seq data.
P2860
Q26744071-3BB0D13D-8754-4490-AE74-5C805423FCF6Q28602489-52EC1580-DB34-41FF-AFA7-5723D560D618Q30275293-A722AD0C-8A64-4A3C-B667-58552286BB84Q30301043-805A28FA-6809-4330-B328-1AC7F6A23704Q30871031-1646150D-5891-4429-8DD9-6E7151FFA7BAQ30920663-68DB5137-D020-4635-BA76-5FA7B49AD16AQ30986279-F715F88C-76A6-44D0-ABD3-6438C35BB41DQ30993335-3E5879F5-481E-44B9-A1D0-3BC6EF15F18EQ31139217-FD8DB14B-68FE-46B9-9163-4F4CAEB360CBQ33878991-7900818A-B05C-4E81-B5DF-436922906CC9Q35763294-E18981EA-D28A-423E-9AF1-8BCA67B384E1Q35931463-3D644AB1-08A7-4247-849C-22422CD1249BQ35958861-A989B4FE-61C5-4754-9D4A-C71F67F138FEQ36083333-FB59335B-25EC-4058-B1F9-155962C0F0D5Q36117938-A51756DE-57FE-4603-A3F0-9C90AEB37558Q36391180-E79D6DB4-8CE4-4F32-9515-F8C06018D847Q37277266-3CF81715-8F73-43F1-A38F-1A4B2AA05E9AQ37706407-2971C064-B8A3-4FAB-8AC2-C0B63DBE9721Q38558121-2FF39A96-CCFD-4A7F-9FA4-A29F0F998866Q38648542-8E4B8A53-B8F3-4385-ABB0-0761EF879EE3Q38702132-F1BADA67-F755-4945-A0AA-3C29C3B02FEDQ39200416-EDA0E81A-92C1-4DCB-A9AF-2CF1420E27FFQ44876533-8E8E4E4F-06BF-46CF-B76F-9F02623D27FDQ47155210-500F329C-DF4A-45B2-93B0-27366D0F8A22Q52364161-56A7D858-93CD-46B7-82F7-128AA54DE013Q53272745-C8FF3957-4F54-42D3-8381-69ECFCE1DEAC
P2860
Transforming RNA-Seq data to improve the performance of prognostic gene signatures.
description
2014 nî lūn-bûn
@nan
2014 թուականի Յունուարին հրատարակուած գիտական յօդուած
@hyw
2014 թվականի հունվարին հրատարակված գիտական հոդված
@hy
2014年の論文
@ja
2014年論文
@yue
2014年論文
@zh-hant
2014年論文
@zh-hk
2014年論文
@zh-mo
2014年論文
@zh-tw
2014年论文
@wuu
name
Transforming RNA-Seq data to improve the performance of prognostic gene signatures.
@ast
Transforming RNA-Seq data to improve the performance of prognostic gene signatures.
@en
type
label
Transforming RNA-Seq data to improve the performance of prognostic gene signatures.
@ast
Transforming RNA-Seq data to improve the performance of prognostic gene signatures.
@en
prefLabel
Transforming RNA-Seq data to improve the performance of prognostic gene signatures.
@ast
Transforming RNA-Seq data to improve the performance of prognostic gene signatures.
@en
P2093
P2860
P1433
P1476
Transforming RNA-Seq data to improve the performance of prognostic gene signatures.
@en
P2093
Barbara Frisch
Harald Binder
Isabella Zwiener
P2860
P304
P356
10.1371/JOURNAL.PONE.0085150
P407
P577
2014-01-08T00:00:00Z