Improved grading and survival prediction of human astrocytic brain tumors by artificial neural network analysis of gene expression microarray data.
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Modulation of HJURP (Holliday Junction-Recognizing Protein) levels is correlated with glioblastoma cells survivalBrain tumor classification using AFM in combination with data mining techniques.Identification of PLP2 and RAB5C as novel TPD52 binding partners through yeast two-hybrid screening.Immune genes are associated with human glioblastoma pathology and patient survival.A Molecular Predictor Reassesses Classification of Human Grade II/III GliomasGlioma IL13Rα2 is associated with mesenchymal signature gene expression and poor patient prognosisNG2 expression in glioblastoma identifies an actively proliferating population with an aggressive molecular signature.Nuclear PKM2 regulates β-catenin transactivation upon EGFR activationMetaQC: objective quality control and inclusion/exclusion criteria for genomic meta-analysis.Improving the Prediction of Survival in Cancer Patients by Using Machine Learning Techniques: Experience of Gene Expression Data: A Narrative Review.Mesenchymal high-grade glioma is maintained by the ID-RAP1 axis.S100B promotes glioma growth through chemoattraction of myeloid-derived macrophagesIntegrin α5β1 and p53 convergent pathways in the control of anti-apoptotic proteins PEA-15 and survivin in high-grade gliomaMeta-analysis of glioblastoma multiforme versus anaplastic astrocytoma identifies robust gene markers.A 16-gene signature distinguishes anaplastic astrocytoma from glioblastoma.Artificial neural networks in neurosurgery.Experiences and expectations for glioma immunotherapeutic approaches.Diffusely infiltrating astrocytomas: pathology, molecular mechanisms and markers.Dual-specificity phosphatase DUSP6 has tumor-promoting properties in human glioblastomas.Molecular profiling of long-term survivors identifies a subgroup of glioblastoma characterized by chromosome 19/20 co-gain.Development of robust discriminant equations for assessing subtypes of glioblastoma biopsiesGene expression signature-based prognostic risk score in patients with glioblastoma.Predicting survival in patients with brain metastases treated with radiosurgery using artificial neural networks.Cox-nnet: An artificial neural network method for prognosis prediction of high-throughput omics data.Ensemble Methods with Voting Protocols Exhibit Superior Performance for Predicting Cancer Clinical Endpoints and Providing More Complete Coverage of Disease-Related Genes.Robust meta-analysis shows that glioma transcriptional subtyping complements traditional approaches.
P2860
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P2860
Improved grading and survival prediction of human astrocytic brain tumors by artificial neural network analysis of gene expression microarray data.
description
2008 nî lūn-bûn
@nan
2008 թուականի Ապրիլին հրատարակուած գիտական յօդուած
@hyw
2008 թվականի ապրիլին հրատարակված գիտական հոդված
@hy
2008年の論文
@ja
2008年論文
@yue
2008年論文
@zh-hant
2008年論文
@zh-hk
2008年論文
@zh-mo
2008年論文
@zh-tw
2008年论文
@wuu
name
Improved grading and survival ...... ne expression microarray data.
@ast
Improved grading and survival ...... ne expression microarray data.
@en
type
label
Improved grading and survival ...... ne expression microarray data.
@ast
Improved grading and survival ...... ne expression microarray data.
@en
prefLabel
Improved grading and survival ...... ne expression microarray data.
@ast
Improved grading and survival ...... ne expression microarray data.
@en
P2093
P2860
P1476
Improved grading and survival ...... ne expression microarray data.
@en
P2093
Anastasis Oulas
Karen Plant
Lawrence P Petalidis
Lisa Happerfield
Magnus Backlund
Tom C Freeman
V Peter Collins
P2860
P304
P356
10.1158/1535-7163.MCT-07-0177
P577
2008-04-29T00:00:00Z