Prediction of clinical outcome in multiple lung cancer cohorts by integrative genomics: implications for chemotherapy selection.
about
Divergent genomic and epigenomic landscapes of lung cancer subtypes underscore the selection of different oncogenic pathways during tumor developmentIntegrative analysis of DNA copy number and gene expression in metastatic oral squamous cell carcinoma identifies genes associated with poor survivalA pseudo-R2 measure for selecting genomic markers with crossing hazards functions.Differential co-expression framework to quantify goodness of biclusters and compare biclustering algorithmsA novel molecular signature identified by systems genetics approach predicts prognosis in oral squamous cell carcinomaA ν-support vector regression based approach for predicting imputation quality.Integrative genomics in combination with RNA interference identifies prognostic and functionally relevant gene targets for oral squamous cell carcinoma.How close are we to customizing chemotherapy in early non-small cell lung cancer?Are we ready to use biomarkers for staging, prognosis and treatment selection in early-stage non-small-cell lung cancer?IGDB.NSCLC: integrated genomic database of non-small cell lung cancerIntegrating the multiple dimensions of genomic and epigenomic landscapes of cancer.Chapter 13: CISNET lung models: comparison of model assumptions and model structures.Stromal responses among common carcinomas correlated with clinicopathologic featuresHow is gene-expression profiling going to challenge the future management of lung cancer?KRAS protein stability is regulated through SMURF2: UBCH5 complex-mediated β-TrCP1 degradation.Genomic medicine in non-small cell lung cancer: paving the path to personalized care.Common pathogenic mechanisms and pathways in the development of COPD and lung cancer.Down-regulation of LATS2 in non-small cell lung cancer promoted the growth and motility of cancer cells.Analysis of bypass signaling in EGFR pathway and profiling of bypass genes for predicting response to anticancer EGFR tyrosine kinase inhibitors.Network modeling of the transcriptional effects of copy number aberrations in glioblastoma.Integrative analysis for the discovery of lung cancer serological markers and validation by MRM-MS.Improved high-dimensional prediction with Random Forests by the use of co-data.[Application of bio-chip in lung cancer research].
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P2860
Prediction of clinical outcome in multiple lung cancer cohorts by integrative genomics: implications for chemotherapy selection.
description
2009 nî lūn-bûn
@nan
2009年の論文
@ja
2009年学术文章
@wuu
2009年学术文章
@zh-cn
2009年学术文章
@zh-hans
2009年学术文章
@zh-my
2009年学术文章
@zh-sg
2009年學術文章
@yue
2009年學術文章
@zh
2009年學術文章
@zh-hant
name
Prediction of clinical outcome ...... ns for chemotherapy selection.
@en
Prediction of clinical outcome ...... ns for chemotherapy selection.
@nl
type
label
Prediction of clinical outcome ...... ns for chemotherapy selection.
@en
Prediction of clinical outcome ...... ns for chemotherapy selection.
@nl
prefLabel
Prediction of clinical outcome ...... ns for chemotherapy selection.
@en
Prediction of clinical outcome ...... ns for chemotherapy selection.
@nl
P2093
P1433
P1476
Prediction of clinical outcome ...... ns for chemotherapy selection.
@en
P2093
Dhinoth Bangarusamy
Elaine Lim
Jean-François Régnard
Lance D Miller
Marco Alifano
Marianne Tuefferd
Maxime Battistella
Patrick Tan
Philippe Broët
Shenli Zhang
P304
P356
10.1158/0008-5472.CAN-08-1116
P407
P577
2009-01-27T00:00:00Z