Risk prediction for late-stage ovarian cancer by meta-analysis of 1525 patient samples. - sprookjes - yvandenbroek - TriplyDB

Risk prediction for late-stage ovarian cancer by meta-analysis of 1525 patient samples.

Risk prediction for late-stage ovarian cancer by meta-analysis of 1525 patient samples.

http://www.wikidata.org/entity/Q36087199

about

Rethinking ovarian cancer II: reducing mortality from high-grade serous ovarian cancer Role of aggressive surgical cytoreduction in advanced ovarian cancer Comparative meta-analysis of prognostic gene signatures for late-stage ovarian cancer Evidence of cancer-promoting roles for AMPK and related kinases Orchestrating high-throughput genomic analysis with Bioconductor A reproducible approach to high-throughput biological data acquisition and integration Cross-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 activation Epithelial ovarian cancer: the molecular genetics of epithelial ovarian cancer A 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 cancer NUAK1 (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 DirGenerank Pooled 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 Cancer Expression 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

Q26780387-437AECAF-67FB-40A3-BE83-B076EC70198C Q26801358-14577C20-4C0A-435E-900C-A56720FE0527 Q26859166-D445D5C3-BB80-4EBF-9266-EC38E907DA96 Q28083964-2C226688-E5E3-4D69-80D7-72204EC3F09F Q28084527-7E32A513-2554-4C14-8E80-4F866F29EBFE Q28647460-7469FF28-1029-4637-8E55-864E68DC6BB0 Q33760371-DF647670-2DFE-40A2-BEDA-EC4DD324A7BE Q34447404-B51456AD-F783-4692-8404-8EE34EE1392B Q34514528-8D99DF40-ED0A-43AD-92B0-504F26060E1E Q35759295-9F5454FA-4ED7-4746-BF80-12B383D45693 Q36072739-C5BE4125-4AA2-47F7-9265-C5672F8C6CE5 Q36182116-67EF35FC-CC6B-43B0-88EB-094EF0FD87A6 Q36199823-AE182547-0AB5-49CE-A00D-3BCE2678F934 Q36360310-71578034-AAF8-4E0D-9056-6739111B97A7 Q36372792-B9D73AE7-6EE3-49FB-863B-50A928DCB927 Q36855239-436427A1-B1A4-4C30-958C-06D4A1C50B01 Q36929682-A884F62E-BE25-4614-80A2-0C47330B1CF0 Q36994579-DBE386B3-E950-41E7-A60E-B871D42F18C4 Q37046579-C9724AB5-1251-4987-AC0B-512508E07B76 Q37094076-D5383687-8046-4A8A-B759-1E209E512AF3 Q37101303-84F3CBD2-0802-4365-B19F-AB81FA0C5058 Q37174032-D71A492E-EF8A-41A4-9D75-F73844BB1896 Q37368631-F2416162-F634-491B-B1E9-6F13C5F48AC7 Q37397132-7B41FF7D-B0AD-4FFD-B873-5F1BBB1DB6AF Q37688907-BBE5C25F-FC67-481A-B4C2-F80A85F5DD39 Q38725310-E15ADC32-D886-40C7-83E6-F61545A4F987 Q38747875-DE5585AA-F9BC-4DE5-AF4E-D151626A8C65 Q39169603-FD37331C-8819-4AAE-98FE-F5CAF32725AA Q41512848-74DA3133-9ED2-41C1-9B3F-A9CB8497C170 Q42276827-1B42D10E-BA75-4CF4-B465-23BBD103DBBA Q42317468-D2911D02-2E4A-4FF5-95FB-57C82D26E3C7 Q42608173-31EA2111-A4C7-461B-B152-A6279850E3FE Q42695489-93577EAF-62F2-4003-B419-F6FD07D37521 Q42923164-68C6FF2C-95CB-4557-926F-08C6CB4B99EB Q45933729-46443E8F-D81D-4643-9A65-6DFD85CECB74 Q46325180-9FCB2F45-1A67-472D-9DE1-1675408F1C99 Q47110118-8CA78D7A-9DB3-4F25-BD02-F6381BC1A627 Q47281811-F351FBF2-D30F-4C7D-853F-C20F80917074 Q47398937-01ECF2F1-3977-4EF6-8810-DEEE3BAB5BDA Q47652919-FA956A77-AA81-4FD9-AEB8-11B185A197ED

P2860

Risk prediction for late-stage ovarian cancer by meta-analysis of 1525 patient samples.

http://www.wikidata.org/entity/Q36087199

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

P1433

Q36087199-5482F670-67DD-4E16-BBCA-8CCCA48B1F14

P1476

Q36087199-BFF9BEF7-D8C7-4323-9A4C-20CF90C7621C

P2093

Q36087199-25604BDC-9BE5-48B1-B6AA-15B3CA48631F

Q36087199-63C5152D-963E-4974-9C7E-A36A98DBE56F

Q36087199-743F7CFF-815F-4AB0-96F8-D41926554D97

Q36087199-7FAB6C59-50DC-4987-8DAD-C0D259D5D2D6

Q36087199-8CC4C593-B9D0-4B72-A707-8464CA70BBDF

Q36087199-9FBE3668-16FB-461F-89A0-92C53597B0D9

P2860

Q36087199-07BCF90F-C342-403A-B2C3-EA6ADBAEB9EF

Q36087199-11C58BBC-57F1-440F-AE25-F1CB3D558434

Q36087199-12854F07-DD94-4A42-9EDC-E0DEA51759EF

Q36087199-13E5834C-B8F5-47B0-9227-4CF3651703CC

Q36087199-1B862B19-4F69-4C9A-B474-1D08469F1041

Q36087199-1F0A7CB9-8C12-4692-9EFF-C339FCB600E6

Q36087199-2476EB02-51FF-4AE9-83BB-0A43BEFEBB08

Q36087199-316ACB72-F44F-4AEE-9D0F-BCA1D0238047

Q36087199-33FEB5A1-06D3-4C15-BFF3-0AF037809859

Q36087199-34F0F33E-BC09-4E3D-A889-193D0F793107

P31

Q36087199-02BF115C-4F33-4EAC-8A20-1F6BC48B5CD4

Q36087199-38b84174-bfbe-427c-a456-6752ec302488

P356

Q36087199-F9900E7F-C5D5-4050-919D-7DC6A6E3A567

P407

Q36087199-8BF58A8F-CA8E-4001-94C5-38C368B6EEF2

P433

Q36087199-F2E391EC-3013-420B-B5BA-38F3C5724520

P478

Q36087199-8B5DC6AD-120E-4C2B-B9A1-5070D289DDDA

P50

Q36087199-0D7B6287-05FD-4359-B271-2F5B62A10716

Q36087199-59F287B4-BE34-4391-A219-4A7FB8471FDA

Q36087199-8392345C-F002-454E-8D15-7E593410CA3A

Q36087199-871FAD40-B8F6-40AF-BA78-5946E8D1CB67

Q36087199-91829AB5-22C5-4190-809E-C62BAC96BFC7

Q36087199-964B59CB-D386-468F-AB03-36B595DF6663

P577

Q36087199-C23C2559-E8C2-4816-B706-FE7BE788CB64

P5875

Q36087199-D9433C19-9CEF-46B9-BA87-BB1626989F27

P698

Q36087199-911CB7D0-8568-4064-8443-CA48C51B0C3D

P921

Q36087199-2E20420A-6CE4-480B-B6D2-33EB57C95442

Q36087199-FAC67BD6-3D6B-4A0F-BEC6-FBC13DBE546F

P932

Q36087199-A63E3355-4BDF-4809-BAC2-0D38ED34C016

P356

P1433

Journal of the National Cancer Institute

P1476

Risk prediction for late-stage ovarian cancer by meta-analysis of 1525 patient samples

@en

P2093

Curtis Huttenhower

http://www.w3.org/2001/XMLSchema#string

Esther Oliva

http://www.w3.org/2001/XMLSchema#string

Franziska Michor

http://www.w3.org/2001/XMLSchema#string

Giovanni Parmigiani

http://www.w3.org/2001/XMLSchema#string

Lorenzo Trippa

http://www.w3.org/2001/XMLSchema#string

Michael J Birrer

http://www.w3.org/2001/XMLSchema#string

P2860

Hypoxia-induced lysyl oxidase is a critical mediator of bone marrow cell recruitment to form the premetastatic niche

Comparative meta-analysis of prognostic gene signatures for late-stage ovarian cancer

Integrated genomic analyses of ovarian carcinoma

Cancer statistics, 2012

Lysyl oxidase plays a critical role in endothelial cell stimulation to drive tumor angiogenesis

Angiogenic mRNA and microRNA gene expression signature predicts a novel subtype of serous ovarian cancer

Comparing the areas under two or more correlated receiver operating characteristic curves: a nonparametric approach

Tackling the widespread and critical impact of batch effects in high-throughput data

Ligand-independent activation of c-Met by fibronectin and α(5)β(1)-integrin regulates ovarian cancer invasion and metastasis.

curatedOvarianData: clinically annotated data for the ovarian cancer transcriptome

P31

scholarly article

P356

10.1093/JNCI/DJU048

http://www.w3.org/2001/XMLSchema#string

P407

P433

5

http://www.w3.org/2001/XMLSchema#string

P478

106

http://www.w3.org/2001/XMLSchema#string

P50

Aedin C Culhane

P577

2014-04-03T00:00:00Z

http://www.w3.org/2001/XMLSchema#dateTime

P5875

261374936

http://www.w3.org/2001/XMLSchema#string

P698

24700803

http://www.w3.org/2001/XMLSchema#string

P921

P932

4580556

http://www.w3.org/2001/XMLSchema#string