Improving gene array analysis: The application of artificial intelligence identifies novel biomarkers of non invasive papillary bladder cancer progression

Improving gene array analysis: The application of artificial intelligence identifies novel biomarkers of non invasive papillary bladder cancer progression

Λεπτομέρειες βιβλιογραφικής εγγραφής
Κύριοι συγγραφείς:	Wild, P, Catto, J, Abbod, M, Linkens, D, Herr, A, Pilarsky, C, Wissmann, C, Hamdy, F, Hartmann, A
Μορφή:	Journal article
Έκδοση:	2007

Παρόμοια τεκμήρια

Artificial intelligence and bladder cancer arrays.
ανά: Wild, P, κ.ά.
Έκδοση: (2007)

Artificial intelligence technique for gene expression profiling of urinary bladder cancer
ανά: Abbod, M, κ.ά.
Έκδοση: (2006)

Artificial intelligence technique for gene expression profiling of urinary bladder cancer
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Έκδοση: (2006)

Artificial intelligence re-analysis of gene array data identifies novel markers of urothelial cancer progression
ανά: Catto, J, κ.ά.
Έκδοση: (2007)

The application of artificial intelligence to microarray data: identification of a novel gene signature to identify bladder cancer progression.
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Έκδοση: (2010)

Artificial intelligence for the prediction bladder cancer
ανά: Abbod, M, κ.ά.
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Comparative study of intelligent models for the prediction of bladder cancer progression.
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Neuro-fuzzy modeling: an accurate and interpretable method for predicting bladder cancer progression.
ανά: Catto, J, κ.ά.
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Promoter hypermethylation identifies progression risk in bladder cancer.
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Artificial intelligence in predicting bladder cancer outcome: a comparison of neuro-fuzzy modeling and artificial neural networks.
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Application of artificial intelligence to the management of urological cancer
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Neurofuzzy modeling to determine recurrence risk following radical cystectomy for nonmetastatic urothelial carcinoma of the bladder.
ανά: Catto, J, κ.ά.
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Development of a prognostic marker panel in bladder cancer using aberrant promoter methylation
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Predictive modeling in cancer: where systems biology meets the stock market.
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Less is more: artificial intelligence and gene-expression arrays.
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Reply from Authors re: David J. DeGraff. Novel Use of a Combined Artificial Intelligence Approach to Identify Patients with Noninvasive Urothelial Cell Carcinoma of the Urinary Bladder Who Are at Greatest Risk for Progression to Muscle-Invasive Disease: A Step Forward. Eur Urol 2010; 57: 407-8
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Aberrant promoter methylation in pre-malignant and invasive tumours of the urinary bladder
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Predictive biomarkers in muscle-invasive bladder cancer
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Targeting chemotherapy to advanced bladder cancer patients most likely to benefit.
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Genetic instability and transitional cell carcinoma of the bladder.
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THE HYPOXIC REGULATION OF FGFR3 IN NON MUSCLE INVASIVE BLADDER CANCER
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FGFR3 mutations indicate better survival in invasive upper urinary tract and bladder tumours.
ανά: van Oers, J, κ.ά.
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Role of Re-Resection in Non–Muscle-Invasive Bladder Cancer
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ανά: Qianxing Mo, κ.ά.
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Metabolomics of Non-muscle Invasive Bladder Cancer: Biomarkers for Early Detection of Bladder Cancer
ανά: Xiangming Cheng, κ.ά.
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Bioinformatics Analysis Identifying Key Biomarkers in Bladder Cancer
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Papillary and muscle invasive bladder tumors with distinct genomic stability profiles have different DNA repair fidelity and KU DNA-binding activities.
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URINARY MICRORNA IN BLADDER CANCER - A POTENTIAL DIAGNOSTIC TOOL
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Non-muscle invasive bladder cancer biomarkers beyond morphology
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The Influence of Inverted Growth Pattern on Recurrence for Patients with Non-Invasive Low Grade Papillary Urothelial Carcinoma of Bladder
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Invasive papillary carcinoma of the breast
ανά: Shijing Wang, κ.ά.
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Carcinomas in the bladder with papillary and pseudopapillary morphology - not always urothelial
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RNA-seq profiling identified a three-lncRNA panel in serum as potential biomarker for muscle-invasive bladder cancer
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