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Cancer informatics

Showing results (211-220 of 716) with videos related to

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Cancer Informatics|April 8, 2022
Deciphering the Increased Prevalence of TP53 Mutations in Metastatic Prostate CancerWensheng Zhang, Yan Dong, Oliver Sartor, et al.
Cancer Informatics|March 31, 2022
<i>In silico</i> Prediction on the PI3K/AKT/mTOR Pathway of the Antiproliferative Effect of <i>O. joconostle</i> in Breast Cancer ModelsAlejandra Ortiz-González, Pedro Pablo González-Pérez, Maura Cárdenas-García, et al.
Cancer Informatics|May 21, 2009
Global tumor RNA expression in early establishment of experimental tumor growth and related angiogenesis following COX-inhibition evaluated by microarray analysisHans Axelsson, Christina Lönnroth, Marianne Andersson, et al.
Cancer Informatics|May 21, 2009
Development of query strategies to identify a histologic lymphoma subtype in a large linked database systemMichael Graiser, Susan G Moore, Rochelle Victor, et al.
Cancer Informatics|May 21, 2009
A novel cross-disciplinary multi-institute approach to translational cancer research: lessons learned from Pennsylvania Cancer Alliance Bioinformatics Consortium (PCABC)Ashokkumar A Patel, John R Gilbertson, Louise C Showe, et al.
Cancer Informatics|May 21, 2009
Comparison of supervised classification methods for protein profiling in cancer diagnosisNadège Dossat, Alain Mangé, Jérôme Solassol, et al.
Cancer Informatics|May 21, 2009
Comparison of computational algorithms for the classification of liver cancer using SELDI mass spectrometry: a case studyChangyu Shen, Timothy E Breen, Lacey E Dobrolecki, et al.
Cancer Informatics|May 21, 2009
Public databases and software for the pathway analysis of cancer genomesIvy F L Tsui, Raj Chari, Timon P H Buys, et al.
Cancer Informatics|May 21, 2009
Germinal center B cell-like (GCB) and activated B cell-like (ABC) type of diffuse large B cell lymphoma (DLBCL): analysis of molecular predictors, signatures, cell cycle state and patient survivalS Blenk, J Engelmann, M Weniger, et al.
Cancer Informatics|May 21, 2009
Estimating the false discovery rate using mixed normal distribution for identifying differentially expressed genes in microarray data analysisAkihiro Hirakawa, Yasunori Sato, Takashi Sozu, et al.
Pageof 72

Showing results (211-220 of 716) with videos related to

Sort By:
Pageof 72
Cancer Informatics|April 8, 2022
Deciphering the Increased Prevalence of TP53 Mutations in Metastatic Prostate CancerWensheng Zhang, Yan Dong, Oliver Sartor, et al.
Cancer Informatics|March 31, 2022
<i>In silico</i> Prediction on the PI3K/AKT/mTOR Pathway of the Antiproliferative Effect of <i>O. joconostle</i> in Breast Cancer ModelsAlejandra Ortiz-González, Pedro Pablo González-Pérez, Maura Cárdenas-García, et al.
Cancer Informatics|May 21, 2009
Global tumor RNA expression in early establishment of experimental tumor growth and related angiogenesis following COX-inhibition evaluated by microarray analysisHans Axelsson, Christina Lönnroth, Marianne Andersson, et al.
Cancer Informatics|May 21, 2009
Development of query strategies to identify a histologic lymphoma subtype in a large linked database systemMichael Graiser, Susan G Moore, Rochelle Victor, et al.
Cancer Informatics|May 21, 2009
A novel cross-disciplinary multi-institute approach to translational cancer research: lessons learned from Pennsylvania Cancer Alliance Bioinformatics Consortium (PCABC)Ashokkumar A Patel, John R Gilbertson, Louise C Showe, et al.
Cancer Informatics|May 21, 2009
Comparison of supervised classification methods for protein profiling in cancer diagnosisNadège Dossat, Alain Mangé, Jérôme Solassol, et al.
Cancer Informatics|May 21, 2009
Comparison of computational algorithms for the classification of liver cancer using SELDI mass spectrometry: a case studyChangyu Shen, Timothy E Breen, Lacey E Dobrolecki, et al.
Cancer Informatics|May 21, 2009
Public databases and software for the pathway analysis of cancer genomesIvy F L Tsui, Raj Chari, Timon P H Buys, et al.
Cancer Informatics|May 21, 2009
Germinal center B cell-like (GCB) and activated B cell-like (ABC) type of diffuse large B cell lymphoma (DLBCL): analysis of molecular predictors, signatures, cell cycle state and patient survivalS Blenk, J Engelmann, M Weniger, et al.
Cancer Informatics|May 21, 2009
Estimating the false discovery rate using mixed normal distribution for identifying differentially expressed genes in microarray data analysisAkihiro Hirakawa, Yasunori Sato, Takashi Sozu, et al.
Pageof 72