Most random gene expression signatures are significantly associated with breast cancer outcome.
about
Concise review: new paradigms for Down syndrome research using induced pluripotent stem cells: tackling complex human genetic diseaseThe path to routine use of genomic biomarkers in the cancer clinicPattern recognition in bioinformaticsComparative meta-analysis of prognostic gene signatures for late-stage ovarian cancerMathematical models of the steps involved in the systemic delivery of a chemotherapeutic to a solid tumor: From circulation to survivalProgesterone receptor modulates ERα action in breast cancer.Signatures of breast cancer metastasis at a glanceIdentifying Novel Biomarkers in Sarcoidosis Using Genome-Based ApproachesSystems biology of cancer: entropy, disorder, and selection-driven evolution to independence, invasion and "swarm intelligence"Biasogram: visualization of confounding technical bias in gene expression dataA genome-wide systematic analysis reveals different and predictive proliferation expression signatures of cancerous vs. non-cancerous cellsPhysioSpace: relating gene expression experiments from heterogeneous sources using shared physiological processesA prognostic gene signature for metastasis-free survival of triple negative breast cancer patientsProof of the concept to use a malignant B cell line drug screen strategy for identification and weight of melphalan resistance genes in multiple myelomaInduction of Wnt-inducible signaling protein-1 correlates with invasive breast cancer oncogenesis and reduced type 1 cell-mediated cytotoxic immunity: a retrospective studyPathway-Based Genomics Prediction using Generalized Elastic NetMolecular portraits: the evolution of the concept of transcriptome-based cancer signaturesThe cure: design and evaluation of a crowdsourcing game for gene selection for breast cancer survival predictionAssociation of Protein Translation and Extracellular Matrix Gene Sets with Breast Cancer Metastasis: Findings Uncovered on Analysis of Multiple Publicly Available Datasets Using Individual Patient Data ApproachInferring gene dependency network specific to phenotypic alteration based on gene expression data and clinical information of breast cancerComprehensive evaluation of published gene expression prognostic signatures for biomarker-based lung cancer clinical studies.A systematic evaluation of multi-gene predictors for the pathological response of breast cancer patients to chemotherapy.TMA Navigator: Network inference, patient stratification and survival analysis with tissue microarray dataAnalyse multiple disease subtypes and build associated gene networks using genome-wide expression profiles.A data similarity-based strategy for meta-analysis of transcriptional profiles in cancer.Extracting insights from the shape of complex data using topology.SurvExpress: an online biomarker validation tool and database for cancer gene expression data using survival analysisEvaluating gene set enrichment analysis via a hybrid data model.Breast cancer prognosis risk estimation using integrated gene expression and clinical dataA genome-wide approach to link genotype to clinical outcome by utilizing next generation sequencing and gene chip data of 6,697 breast cancer patients.Comparative study of joint analysis of microarray gene expression data in survival prediction and risk assessment of breast cancer patients.Data-Driven Metabolic Pathway Compositions Enhance Cancer Survival Prediction.Prognostic value of gene signatures and proliferation in lymph-node-negative breast cancerThe CO-Regulation Database (CORD): a tool to identify coordinately expressed genesMultivariate Feature Selection of Image Descriptors Data for Breast Cancer with Computer-Assisted DiagnosisSelective gene-expression profiling of migratory tumor cells in vivo predicts clinical outcome in breast cancer patients.Robust clinical outcome prediction based on Bayesian analysis of transcriptional profiles and prior causal networksMELK is an oncogenic kinase essential for mitotic progression in basal-like breast cancer cells.Identification of Jak-STAT signaling involvement in sarcoidosis severity via a novel microRNA-regulated peripheral blood mononuclear cell gene signature.An integrated genomic approach identifies persistent tumor suppressive effects of transforming growth factor-β in human breast cancer
P2860
Q22241147-89532FF8-AD05-420E-B4B3-A587FB1F7C8CQ26782733-7E709A1A-C3F5-483E-BF7F-B3781D80F876Q26852625-E1CB2702-6112-4223-9BFB-FF7E7F8FD805Q26859166-D71C0597-4BD4-4530-B015-05244D076A16Q27023583-24AEFDE4-D0C4-4257-A032-B03B909F21CBQ27316268-6DA2D794-91CC-4006-9744-3D82B70E0F5BQ28071451-514ACE56-391F-4C44-974B-EF498635EBAAQ28082850-652F4407-C66A-49BF-A7D2-615587E87983Q28391909-FF06F89C-73F0-42DA-8B8C-13B4471F1DE5Q28486222-9053111A-BBB6-4B16-A72F-E23FC092449FQ28533665-99614508-A991-458C-AC25-ECCFD6B5E1F3Q28534493-9157A126-B814-4E2D-9B98-983D51F6E9FBQ28536791-ED57E4DC-0C42-4E2D-999F-B709118F89F8Q28537857-065794EA-2D62-4468-9240-5FD42D39C3F3Q28538654-3963F47E-3298-4295-B38F-AE36B124BC0CQ28550614-490C07B7-8A44-4E3A-B4C5-29A85C7EC188Q28607220-62613BC7-8023-4C5C-A16A-CCC990670FD6Q28651478-9B07C7D3-8226-4B0C-BED9-4504E247BA97Q30000323-2F130AE2-D2B8-4471-846C-BBF553D3B1F1Q30000682-7772B886-B0CD-4D1C-841A-8F376EA1D8E1Q30235944-7934ED42-60E9-45A5-B602-D2796B0CB0ADQ30423830-B1B830F7-FFAB-400C-AE4C-2D74306A6723Q30486190-57B35CE1-2F55-4933-9884-34306F8D9726Q30488013-2D571486-DC59-44BD-A7B4-24B7716DA99DQ30587056-F113AA0C-1DB6-4E02-BA80-DF9D6694063BQ30587445-9087F9BB-8B75-496C-B15F-3F59094CA9BFQ30670039-71E416C4-5411-4FF7-BDE5-D093D7393E3DQ30761703-F4D33BF8-3E99-4D0E-A87C-6FF8B1BA2330Q30832440-903FBDB9-E4BD-4FB7-8815-986612D4D23AQ31006978-BCC39529-BD71-474F-919C-003EC4B4293CQ31011613-6A071E1A-879E-4F6A-B9FF-B801C4A6E37AQ31133387-03D32701-AC2E-4089-92AF-2AB46188A731Q31152670-2B62489B-27E4-44A7-B2AE-6A0F48A9BE7BQ31152739-731BC5A4-8955-485C-A306-C5675F7042D6Q31163769-5BD85E6B-9DB3-4E18-A97D-F6661FF0A8A7Q33739307-94046CD6-BC62-484E-BC5F-0B747E33072DQ33760414-0CB54067-F6AA-4353-9EE0-2871DB4494D2Q33762222-C19AFFB6-EBC2-447F-926E-2418DA731F75Q33835227-CE6B98BB-0275-4892-97B4-B367051A9E97Q33892460-10602851-21B9-4A9E-8FAD-861085DEE408
P2860
Most random gene expression signatures are significantly associated with breast cancer outcome.
description
2011 nî lūn-bûn
@nan
2011 թուականի Հոկտեմբերին հրատարակուած գիտական յօդուած
@hyw
2011 թվականի հոտեմբերին 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
Most random gene expression si ...... ed with breast cancer outcome.
@ast
Most random gene expression si ...... ed with breast cancer outcome.
@en
Most random gene expression si ...... ed with breast cancer outcome.
@nl
type
label
Most random gene expression si ...... ed with breast cancer outcome.
@ast
Most random gene expression si ...... ed with breast cancer outcome.
@en
Most random gene expression si ...... ed with breast cancer outcome.
@nl
prefLabel
Most random gene expression si ...... ed with breast cancer outcome.
@ast
Most random gene expression si ...... ed with breast cancer outcome.
@en
Most random gene expression si ...... ed with breast cancer outcome.
@nl
P2093
P2860
P1476
Most random gene expression si ...... ed with breast cancer outcome.
@en
P2093
David Venet
Jacques E Dumont
Vincent Detours
P2860
P304
P356
10.1371/JOURNAL.PCBI.1002240
P577
2011-10-20T00:00:00Z