Gene expression profile of adult T-cell acute lymphocytic leukemia identifies distinct subsets of patients with different response to therapy and survival.
about
Bioconductor: open software development for computational biology and bioinformaticsHigh CD45 surface expression determines relapse risk in children with precursor B-cell and T-cell acute lymphoblastic leukemia treated according to the ALL-BFM 2000 protocolCRIF1 interacting with CDK2 regulates bone marrow microenvironment-induced G0/G1 arrest of leukemia cellsGene expression profiling identifies a subset of adult T-cell acute lymphoblastic leukemia with myeloid-like gene features and over-expression of miR-223Graft-versus-tumor response in patients with multiple myeloma is associated with antibody response to BCMA, a plasma-cell membrane receptorGenes contributing to minimal residual disease in childhood acute lymphoblastic leukemia: prognostic significance of CASP8AP2From cytopenia to leukemia: the role of Gfi1 and Gfi1b in blood formationSystems analysis of high-throughput dataRNA-Seq and microarrays analyses reveal global differential transcriptomes of Mesorhizobium huakuii 7653R between bacteroids and free-living cellsDisruption of epidermal specific gene expression and delayed skin development in AP-2 gamma mutant miceFunctional cohesion of gene sets determined by latent semantic indexing of PubMed abstractsTen years of pathway analysis: current approaches and outstanding challengesProteomic classification of acute leukemias by alignment-based quantitation of LC-MS/MS data sets.Empirical evaluation of consistency and accuracy of methods to detect differentially expressed genes based on microarray data.Identification of high-copper-responsive target pathways in Atp7b knockout mouse liver by GSEA on microarray data sets.Improved scoring of functional groups from gene expression data by decorrelating GO graph structure.Comparison and evaluation of methods for generating differentially expressed gene lists from microarray data.Importance of data structure in comparing two dimension reduction methods for classification of microarray gene expression data.Domain-enhanced analysis of microarray data using GO annotations.A Robust and Efficient Feature Selection Algorithm for Microarray Data.Meta-analysis of several gene lists for distinct types of cancer: a simple way to reveal common prognostic markers.Murine leukemias with retroviral insertions at Lmo2 are predictive of the leukemias induced in SCID-X1 patients following retroviral gene therapy.Testing the additional predictive value of high-dimensional molecular data.Dynamic regulation of genetic pathways and targets during aging in Caenorhabditis elegans.A gene expression signature of CD34+ cells to predict major cytogenetic response in chronic-phase chronic myeloid leukemia patients treated with imatinib.Gene expression profiling identifies inflammation and angiogenesis as distinguishing features of canine hemangiosarcoma.AHNAK2 is a potential prognostic biomarker in patients with PDAC.rpartOrdinal: An R Package for Deriving a Classification Tree for Predicting an Ordinal ResponseIndependent filtering increases detection power for high-throughput experimentsSegMine workflows for semantic microarray data analysis in Orange4WSThe cross-validated AUC for MCP-logistic regression with high-dimensional data.ETV6 mutations in early immature human T cell leukemiasIdentification of genes potentially involved in disease transformation of CML.Interleukin-8 (CXCL8) production is a signatory T cell effector function of human newborn infants.Identification of key pathways and transcription factors related to Parkinson disease in genome wide.The use of microarray technologies in clinical oncologyA Comparative Study of Mouse Hepatic and Intestinal Gene Expression Profiles under PPARα Knockout by Gene Set Enrichment AnalysisInhibition of IRAK1/4 sensitizes T cell acute lymphoblastic leukemia to chemotherapies.Comprehensive identification of essential pathways and transcription factors related to epilepsy by gene set enrichment analysis on microarray datasets.A feature selection method for classification within functional genomics experiments based on the proportional overlapping score.
P2860
Q21194861-C236DE5F-DD34-4AAE-8F38-BCB11EF244EBQ23749304-F3B62384-9AF0-4925-AC28-B56A48998ED8Q24336990-EEF20CF9-1F7C-4318-89E2-CF204C353B2FQ24624017-43BCC573-E121-49D9-8705-5829134D7E2BQ24670205-643B116C-7CC5-4785-B0AD-5FEF9111B93FQ24685514-C30CD958-45EA-40BF-AB6C-88B6CE7E387AQ26786192-4478E977-D154-4515-A864-BBED7BD85CC5Q27023883-A699E13E-2318-4FDB-8953-D8D9DDF65F6AQ28541792-C1E21D8E-4F99-4668-8CD0-DA35E9B9B830Q28590539-A50DB3F3-5426-4D06-AD03-F0BF1E37209AQ28741020-538311E7-A7BC-44CB-B0B5-11C46D68D66FQ29615564-2E712631-4F95-4432-89F7-09EB45CB10EBQ30558138-15EABF23-F511-43EF-B357-BA7AFC5EC46BQ30757422-E1E8BC7C-76AA-4402-BCB5-EF6C54F38055Q31035638-751A6594-F93A-4CF8-B46F-93E141578D7BQ31036410-2C76C705-AF43-4564-87A7-3426BAB86E66Q31050008-C41535A4-04CA-4905-B13B-3A2DA6DA46C0Q31105470-164BDBA0-C5F2-4588-A605-BF7CADAD9291Q31106614-684742BD-E310-4918-959B-1A347CBEE4F3Q31150389-F357028E-1899-4D59-A6AC-A8FBF185C09EQ33268788-DE5B4BD2-6923-46E2-808F-F9B101153910Q33450585-C79EE75C-ACAD-4A6F-9E94-01301FFEC523Q33530410-A05B8976-4321-458C-A785-70C27839E6D6Q33577722-3B5A9235-D2A8-4AE3-A99A-5D3DC05EAB23Q33597519-EB82D09E-0100-41DE-A3F3-B3B0DAE614BEQ33742809-5DC5FDD2-D975-4138-80B6-B49EF2A11CD0Q33761904-7E530702-0AAE-4C67-BE26-C5CCB5942C88Q33973523-8F2763D1-42DE-4A3E-9D66-AE6EF5AB0848Q34006510-320BE854-B2AA-4DFB-B2A5-7B9BB2947214Q34058962-D938A4F4-13C0-473F-B246-6B1E52258B37Q34086293-B71A977B-B740-4A03-A4CD-6C62B70FF7F4Q34240267-243B0FA6-1B3B-4A9C-893A-A1A73EB2EA25Q34409580-28581014-F4A5-4B6F-9499-6C879B5E6D39Q34439778-6FA31EDB-E209-4B06-89D9-D1F9C1CE3EE7Q34449994-065FF710-7730-457A-B7CA-476508C550ADQ34496896-5B6E87FE-F4DC-46DA-922A-58B2ECC67A1DQ35138153-9FC81A63-9FBA-4904-B3A1-2D38B8EE5E93Q35183900-94B64F61-4214-48F8-BF41-D7D8630174BEQ35205988-0316E256-50AD-4A46-8DF8-B183A4E5717AQ35223193-5C3EBA15-67FD-4813-96CB-D655D598097B
P2860
Gene expression profile of adult T-cell acute lymphocytic leukemia identifies distinct subsets of patients with different response to therapy and survival.
description
2003 nî lūn-bûn
@nan
2003年の論文
@ja
2003年学术文章
@wuu
2003年学术文章
@zh-cn
2003年学术文章
@zh-hans
2003年学术文章
@zh-my
2003年学术文章
@zh-sg
2003年學術文章
@yue
2003年學術文章
@zh
2003年學術文章
@zh-hant
name
Gene expression profile of adu ...... ponse to therapy and survival.
@en
Gene expression profile of adu ...... ponse to therapy and survival.
@nl
type
label
Gene expression profile of adu ...... ponse to therapy and survival.
@en
Gene expression profile of adu ...... ponse to therapy and survival.
@nl
prefLabel
Gene expression profile of adu ...... ponse to therapy and survival.
@en
Gene expression profile of adu ...... ponse to therapy and survival.
@nl
P2093
P921
P1433
P1476
Gene expression profile of adu ...... ponse to therapy and survival.
@en
P2093
Antonella Vitale
Franco Mandelli
Jerome Ritz
Marco Vignetti
Sabina Chiaretti
Xiaochun Li
P304
P356
10.1182/BLOOD-2003-09-3243
P407
P577
2003-12-18T00:00:00Z