Methods of Microarray Data Analysis IV
| Autor Corporativo: | |
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| Otros Autores: | , |
| Formato: | eBook |
| Lenguaje: | English |
| Publicado: |
New York, NY :
Springer US : Imprint: Springer,
2005.
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| Edición: | 1st ed. 2005. |
| Materias: | |
| Acceso en línea: | https://doi.org/10.1007/b100565 |
Tabla de Contenidos:
- Cancer: Clinical Challenges and Opportunities
- Gene Expression Data and Survival Analysis
- The Needed Replicates of Arrays in Microarray Experiments for Reliable Statistical Evaluation
- Pooling Information Across Different Studies and Oligonucleotide Chip Types to Identify Prognostic Genes for Lung Cancer
- Application of Survival and Meta-analysis to Gene Expression Data Combined from Two Studies
- Making Sense of Human Lung Carcinomas Gene Expression Data: Integration and Analysis of Two Affymetrix Platform Experiments
- Entropy and Survival-based Weights to Combine Affymetrix Array Types and Analyze Differential Expression and Survival
- Associating Microarray Data with a Survival Endpoint
- Differential Correlation Detects Complex Associations Between Gene Expression and Clinical Outcomes in Lung Adenocarcinomas
- Probabilistic Lung Cancer Models Conditioned on Gene Expression Microarray Data
- Integration of Microarray Data for a Comparative Study of Classifiers and Identification of Marker Genes
- Use of Micro Array Data via Model-based Classification in the Study and Prediction of Survival from Lung Cancer
- Microarray Data Analysis of Survival Times of Patients with Lung Adenocarcinomas Using ADC and K-Medians Clustering
- Higher Dimensional Approach for Classification of Lung Cancer Microarray Data
- Microarray Data Analysis Using Neural Network Classifiers and Gene Selection Methods
- A Combinatorial Approach to the Analysis of Differential Gene Expression Data
- Genes Associated with Prognosis in Adenocarcinoma Across Studies at Multiple Institutions.