Time to recurrence and survival in serous ovarian tumors predicted from integrated genomic profiles.
about
Integration Analysis of Three Omics Data Using Penalized Regression Methods: An Application to Bladder CancerHigh quality genomic copy number data from archival formalin-fixed paraffin-embedded leiomyosarcoma: optimisation of universal linkage system labellingMolecular portraits: the evolution of the concept of transcriptome-based cancer signaturesBreakthroughs in genomics data integration for predicting clinical outcomeIntegrated analyses of microRNAs demonstrate their widespread influence on gene expression in high-grade serous ovarian carcinomaMethods of integrating data to uncover genotype-phenotype interactions.Discovering key regulatory mechanisms from single-factor and multi-factor regulations in glioblastoma utilizing multi-dimensional data.SIFORM: shared informative factor models for integration of multi-platform bioinformatic data.Machine learning and systems genomics approaches for multi-omics data.More Is Better: Recent Progress in Multi-Omics Data Integration MethodsIdentification of two poorly prognosed ovarian carcinoma subtypes associated with CHEK2 germ-line mutation and non-CHEK2 somatic mutation gene signatures.Differing clinical impact of BRCA1 and BRCA2 mutations in serous ovarian cancer.Identifying multi-layer gene regulatory modules from multi-dimensional genomic data.Identification of ovarian cancer associated genes using an integrated approach in a Boolean framework.A network module-based method for identifying cancer prognostic signaturesIntegrative network analysis for survival-associated gene-gene interactions across multiple genomic profiles in ovarian cancer.Integrative eQTL analysis of tumor and host omics data in individuals with bladder cancer.Ovarian cancer : making its own rules-again.Empirical chemosensitivity testing in a spheroid model of ovarian cancer using a microfluidics-based multiplex platform.A METHYLATION-TO-EXPRESSION FEATURE MODEL FOR GENERATING ACCURATE PROGNOSTIC RISK SCORES AND IDENTIFYING DISEASE TARGETS IN CLEAR CELL KIDNEY CANCER.Cancer Progression Prediction Using Gene Interaction Regularized Elastic Net.The ENCODE project and perspectives on pathways.Feature selection through validation and un-censoring of endovascular repair survival data for predicting the risk of re-intervention.Community monitoring for youth violence surveillance: testing a prediction model.SNRFCB: sub-network based random forest classifier for predicting chemotherapy benefit on survival for cancer treatment.Integrating Clinical and Multiple Omics Data for Prognostic Assessment across Human Cancers.Developing Predictive or Prognostic Biomarkers for Charged Particle Radiotherapy
P2860
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P2860
Time to recurrence and survival in serous ovarian tumors predicted from integrated genomic profiles.
description
2011 nî lūn-bûn
@nan
2011 թուականի Նոյեմբերին հրատարակուած գիտական յօդուած
@hyw
2011 թվականի նոյեմբերին հրատարակված գիտական հոդված
@hy
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
name
Time to recurrence and surviva ...... m integrated genomic profiles.
@ast
Time to recurrence and surviva ...... m integrated genomic profiles.
@en
Time to recurrence and surviva ...... m integrated genomic profiles.
@nl
type
label
Time to recurrence and surviva ...... m integrated genomic profiles.
@ast
Time to recurrence and surviva ...... m integrated genomic profiles.
@en
Time to recurrence and surviva ...... m integrated genomic profiles.
@nl
prefLabel
Time to recurrence and surviva ...... m integrated genomic profiles.
@ast
Time to recurrence and surviva ...... m integrated genomic profiles.
@en
Time to recurrence and surviva ...... m integrated genomic profiles.
@nl
P2093
P2860
P1433
P1476
Time to recurrence and surviva ...... m integrated genomic profiles.
@en
P2093
Chris Sander
Douglas A Levine
Parminder K Mankoo
Ronglai Shen
P2860
P304
P356
10.1371/JOURNAL.PONE.0024709
P407
P577
2011-11-03T00:00:00Z