In silico dissection of cell-type-associated patterns of gene expression in prostate cancer
about
Microarray analysis identifies a death-from-cancer signature predicting therapy failure in patients with multiple types of cancerThe RhoGAP protein DLC-1 functions as a metastasis suppressor in breast cancer cellsMacrodissection versus microdissection of rectal carcinoma: minor influence of stroma cells to tumor cell gene expression profiles.In silico microdissection of microarray data from heterogeneous cell populationsDual activation of pathways regulated by steroid receptors and peptide growth factors in primary prostate cancer revealed by Factor Analysis of microarray data.RNA-Seq Differentiates Tumour and Host mRNA Expression Changes Induced by Treatment of Human Tumour Xenografts with the VEGFR Tyrosine Kinase Inhibitor CediranibMeta-analysis of prostate cancer gene expression data identifies a novel discriminatory signature enriched for glycosylating enzymesExpression differences between African American and Caucasian prostate cancer tissue reveals that stroma is the site of aggressive changesAn accurate prostate cancer prognosticator using a seven-gene signature plus Gleason score and taking cell type heterogeneity into accountInferring developmental stage composition from gene expression in human malariaOptimal deconvolution of transcriptional profiling data using quadratic programming with application to complex clinical blood samplesUsing the ratio of means as the effect size measure in combining results of microarray experiments.Tumor-specific silencing of COPZ2 gene encoding coatomer protein complex subunit ζ 2 renders tumor cells dependent on its paralogous gene COPZ1DeMix: deconvolution for mixed cancer transcriptomes using raw measured data.An assessment of computational methods for estimating purity and clonality using genomic data derived from heterogeneous tumor tissue samplesRobust prostate cancer marker genes emerge from direct integration of inter-study microarray data.Complex Sources of Variation in Tissue Expression Data: Analysis of the GTEx Lung TranscriptomeA self-directed method for cell-type identification and separation of gene expression microarrays.A Robust and Efficient Feature Selection Algorithm for Microarray Data.Whole transcriptome amplification for gene expression profiling and development of molecular archives.Down regulation of PSA by C/EBPalpha is associated with loss of AR expression and inhibition of PSA promoter activity in the LNCaP cell lineRobust computational reconstitution - a new method for the comparative analysis of gene expression in tissues and isolated cell fractions.Integrative molecular concept modeling of prostate cancer progression.Integration of genome and chromatin structure with gene expression profiles to predict c-MYC recognition site binding and function.Gene expression profiles of prostate cancer reveal involvement of multiple molecular pathways in the metastatic process.Evidence for systems-level molecular mechanisms of tumorigenesisSample matching by inferred agonal stress in gene expression analyses of the brain.Transcriptional profiling of inductive mesenchyme to identify molecules involved in prostate development and diseaseLaser-capture microdissection in prostate cancer research: establishment and validation of a powerful tool for the assessment of tumour-stroma interactions.Co-regulation analysis of closely linked genes identifies a highly recurrent gain on chromosome 17q25.3 in prostate cancerIntegrative analysis of gene expression data including an assessment of pathway enrichment for predicting prostate cancerThe ordering of expression among a few genes can provide simple cancer biomarkers and signal BRCA1 mutations.Biomarker discovery in heterogeneous tissue samples -taking the in-silico deconfounding approach.An Integrative Genomics Approach for Associating Genome-Wide Association Studies Information With Localized and Metastatic Prostate Cancer PhenotypesIn silico estimates of tissue components in surgical samples based on expression profiling data.Urine-based assays for the detection of bladder cancer.Derivation of cancer diagnostic and prognostic signatures from gene expression data.In silico ascription of gene expression differences to tumor and stromal cells in a model to study impact on breast cancer outcome.Improving cancer classification accuracy using gene pairsStatistical expression deconvolution from mixed tissue samples.
P2860
Q24528253-E7C69B44-2769-4678-A17E-F1BEEA4B1EE2Q24537312-F9AEF0A7-AB7D-4DD1-A0F8-4E8F59289B60Q24810564-52C5AB26-93DC-4C7D-9306-1C9133DD19F9Q24811429-EA2923A1-6860-4A38-977E-593A93F57E44Q24817218-7B662928-A236-4D34-8A30-3703F1ED40C3Q27315803-7EB32975-EE86-4C18-864A-F96AA585C00AQ28085759-FA2FBB08-58BB-4418-AD2F-B586BB4ACC08Q28292423-E9EF3C7C-8C98-4A88-A7FE-CBA3AF8D1FA9Q28484126-A37A7E1A-3E88-47F5-9951-CCAE558B6671Q28536897-A349B7E9-6F7C-42C1-80DA-C460F9212C95Q28742674-B6DB8BD6-B53C-4204-9148-14C56436B70FQ30491973-08187BBE-ECDE-4749-8CEB-51D467A20C14Q30502952-81018B8D-D861-460B-A876-7D1EA7367F4AQ30634335-824276B7-D24B-4078-B564-85DE5ABAB961Q30762666-99093189-13A5-4D05-BB77-F02C517F2DC4Q31002200-F0BE2BC9-5466-4907-8A22-F908E8585C61Q31126351-DF080102-868F-4BCE-A541-B2E6B100E358Q31129201-F4E4B6BF-D454-4BDC-B17A-9143D98DCFC1Q31150389-00857ABE-16D5-48B9-94AF-A84F98AB8EF6Q33239781-18E990E8-02D8-4651-9EE1-D48960ACD38AQ33246793-4870160A-68A2-4ECD-927D-F41207377BA7Q33252920-1CBAE84E-EDC7-42B7-9ED2-743342B53814Q33266777-DF7EA328-D93C-47A4-9FA9-7D23FD075FD4Q33281287-D6B56735-B12B-4305-80EA-4FC825E802F2Q33281964-C1A43B70-B5DE-4885-AF46-B98D983B3844Q33288468-8EDF11DC-8067-47C0-831E-80D53F5B8B8DQ33300170-CC642914-31C2-4732-840F-F06300BCDF4AQ33301957-D18704AC-B531-4525-AAB0-6897718566E7Q33314416-64670AA5-6C3B-4E75-BF19-3783A530309DQ33381372-8286CA9B-D2E8-4B4F-8212-529018124A71Q33449321-5126F76F-A04E-44EE-BD1A-2A1B7E62BC51Q33495401-05886A2E-25DC-45B9-953B-80C33B390231Q33523636-4143E331-9FA9-44AD-8CA7-63CCE911E858Q33565740-B6671225-07A4-4AF3-9BF3-4302342429F3Q33642598-D4350CE9-EDA6-446E-9259-D4CF0EF19E94Q33643957-461F8CE0-AAEA-421B-9D5A-BA05290C068DQ33748879-836B9D83-DDB7-4FC2-8595-BB4C97C14ACBQ33761298-C2C07572-91E5-4494-B1ED-BC36F1EA290FQ33784190-96067597-C1C0-4167-AC53-6D58A6ACF802Q33785569-C3F1CAF1-C014-4284-BA89-8227FA50509E
P2860
In silico dissection of cell-type-associated patterns of gene expression in prostate cancer
description
2004 nî lūn-bûn
@nan
2004年の論文
@ja
2004年論文
@yue
2004年論文
@zh-hant
2004年論文
@zh-hk
2004年論文
@zh-mo
2004年論文
@zh-tw
2004年论文
@wuu
2004年论文
@zh
2004年论文
@zh-cn
name
In silico dissection of cell-t ...... expression in prostate cancer
@ast
In silico dissection of cell-t ...... expression in prostate cancer
@en
type
label
In silico dissection of cell-t ...... expression in prostate cancer
@ast
In silico dissection of cell-t ...... expression in prostate cancer
@en
prefLabel
In silico dissection of cell-t ...... expression in prostate cancer
@ast
In silico dissection of cell-t ...... expression in prostate cancer
@en
P2093
P2860
P50
P356
P1476
In silico dissection of cell-t ...... expression in prostate cancer
@en
P2093
Anne Sawyers
Charles C Berry
Dan Mercola
Igor Klacansky
Iveta Kalcheva
Karen Arden
Linda Wasserman
Robert O Stuart
Steven Goodison
P2860
P304
P356
10.1073/PNAS.2536479100
P407
P577
2004-01-01T00:00:00Z