Computational and Statistical Approaches to Genomics
Computational and Statistical Genomics aims to help researchers deal with current genomic challenges. Topics covered include: overviews of the role of supercomputers in genomics research, the existing challenges and directions in image processing for microarray technology, and web-based tools for mi...
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| Other Authors: | , |
| Format: | Electronic |
| Language: | English |
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Boston, MA :
Springer US,
2002.
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| Subjects: | |
| Online Access: | View fulltext via EzAccess |
Table of Contents:
- Microarray Image Analysis and Gene Expression Ratio Statistics
- Statistical Considerations in the Assessment of cDNA Microarray Data Obtained Using Amplification
- Sources of Variation in Microarray Experiments
- Studentizing Microarray Data
- Exploratory Clustering of Gene Expression Profiles of Mutated Yeast Strains
- Selecting Informative Genes for Cancer Classification Using Gene Expression Data
- Design Issues and Comparison of Methods for Microarray-Based Classification
- Analyzing Protein Sequences Using Signal Analysis Techniques
- Statistics of the Numbers of Transcripts and Protein Sequences Encoded in the Genome
- Normalized Maximum Likelihood Models for Boolean Regression with Application to Prediction and Classification in Genomics
- Inference of Genetic Regulatory Networks Via Best-Fit Extensions
- Regularization and Noise Injection for Improving Genetic Network Models
- Parallel Computation and Visualization Tools for Codetermination Analysis of Multivariate Gene Expression Relations
- Human Glioma Diagnosis from Gene Expression Data
- Application of DNA Microarray Technology to Clinical Biopsies of Breast Cancer
- Alternative Splicing: Genetic Complexity in Cancer
- Single-Nucleotide Polymorphisms, DNA Repair, and Cancer.