Risk prediction for late-stage ovarian cancer by meta-analysis of 1525 patient samples.
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Rethinking ovarian cancer II: reducing mortality from high-grade serous ovarian cancerRole of aggressive surgical cytoreduction in advanced ovarian cancerComparative meta-analysis of prognostic gene signatures for late-stage ovarian cancerEvidence of cancer-promoting roles for AMPK and related kinasesOrchestrating high-throughput genomic analysis with BioconductorA reproducible approach to high-throughput biological data acquisition and integrationCross-study validation for the assessment of prediction algorithms.OvMark: a user-friendly system for the identification of prognostic biomarkers in publically available ovarian cancer gene expression datasets.Single Gene Prognostic Biomarkers in Ovarian Cancer: A Meta-Analysis.Characterization of twenty-five ovarian tumour cell lines that phenocopy primary tumours.Machine Learning Meta-analysis of Large Metagenomic Datasets: Tools and Biological Insights.Elevated AKAP12 in paclitaxel-resistant serous ovarian cancer cells is prognostic and predictive of poor survival in patients.Risk prediction model for epithelial ovarian cancer using molecular markers and clinical characteristics.'Big data' approaches for novel anti-cancer drug discovery.Suboptimal cytoreduction in ovarian carcinoma is associated with molecular pathways characteristic of increased stromal activationEpithelial ovarian cancer: the molecular genetics of epithelial ovarian cancerA transcriptome-based global map of signaling pathways in the ovarian cancer microenvironment associated with clinical outcome.Extracting a low-dimensional description of multiple gene expression datasets reveals a potential driver for tumor-associated stroma in ovarian cancer.Transcriptomic analysis of stage 1 versus advanced adult granulosa cell tumors.Genetic and molecular changes in ovarian cancer.Relational Network for Knowledge Discovery through Heterogeneous Biomedical and Clinical Features.Expression signature distinguishing two tumour transcriptome classes associated with progression-free survival among rare histological types of epithelial ovarian cancerNUAK1 (ARK5) Is Associated with Poor Prognosis in Ovarian Cancer.Efficient Regularized Regression with L0 Penalty for Variable Selection and Network Construction.CD103+ intraepithelial T cells in high-grade serous ovarian cancer are phenotypically diverse TCRαβ+ CD8αβ+ T cells that can be targeted for cancer immunotherapy.Identifying prognostic signature in ovarian cancer using DirGenerankPooled Clustering of High-Grade Serous Ovarian Cancer Gene Expression Leads to Novel Consensus Subtypes Associated with Survival and Surgical Outcomes.The Unique Molecular and Cellular Microenvironment of Ovarian CancerExpression of the chemokine CXCL14 in the tumour stroma is an independent marker of survival in breast cancer.Identification of common oncogenic and early developmental pathways in the ovarian carcinomas controlling by distinct prognostically significant microRNA subsets.The transcriptional signature of human ovarian carcinoma macrophages is associated with extracellular matrix reorganization.Open access to large scale datasets is needed to translate knowledge of cancer heterogeneity into better patient outcomes.Size matters: Survival benefit conferred by intratumoral T cells is dependent on surgical outcome, treatment sequence and T cell differentiation.Caspase 8 expression may determine the survival of women with ovarian cancer.Challenges and Opportunities in Studying the Epidemiology of Ovarian Cancer Subtypes.Predictive modeling for determination of microscopic residual disease at primary cytoreduction: An NRG Oncology/Gynecologic Oncology Group 182 Study.Sparse generalized linear model with L0 approximation for feature selection and prediction with big omics data.Clinicopathologic characteristics associated with long-term survival in advanced epithelial ovarian cancer: an NRG Oncology/Gynecologic Oncology Group ancillary data study.Proteotranscriptomics Reveal Signaling Networks in the Ovarian Cancer Microenvironment.Calcium signalling links MYC to NUAK1.
P2860
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P2860
Risk prediction for late-stage ovarian cancer by meta-analysis of 1525 patient samples.
description
2014 nî lūn-bûn
@nan
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
2014年论文
@zh
2014年论文
@zh-cn
name
Risk prediction for late-stage ovarian cancer by meta-analysis of 1525 patient samples.
@ast
Risk prediction for late-stage ovarian cancer by meta-analysis of 1525 patient samples.
@en
type
label
Risk prediction for late-stage ovarian cancer by meta-analysis of 1525 patient samples.
@ast
Risk prediction for late-stage ovarian cancer by meta-analysis of 1525 patient samples.
@en
prefLabel
Risk prediction for late-stage ovarian cancer by meta-analysis of 1525 patient samples.
@ast
Risk prediction for late-stage ovarian cancer by meta-analysis of 1525 patient samples.
@en
P2093
P2860
P50
P356
P1476
Risk prediction for late-stage ovarian cancer by meta-analysis of 1525 patient samples
@en
P2093
Curtis Huttenhower
Esther Oliva
Franziska Michor
Giovanni Parmigiani
Lorenzo Trippa
Michael J Birrer
P2860
P356
10.1093/JNCI/DJU048
P407
P577
2014-04-03T00:00:00Z