Computational and Statistical Approaches to Genomics

• Συγγραφή απο Οργανισμό/Αρχή: SpringerLink (Online service)
• Άλλοι συγγραφείς: Zhang, Wei (Επιμελητής έκδοσης), Shmulevich, Ilya (Επιμελητής έκδοσης)
• Μορφή: Ηλεκτρονική πηγή Ηλ. βιβλίο
• Γλώσσα: English
• Έκδοση: Boston, MA : Springer US, 2002.
• Θέματα: Life sciences.; Cancer research.; Biotechnology.; Animal anatomy.; Statistics.; Life Sciences.; Animal Anatomy / Morphology / Histology.; Cancer Research.; Signal, Image and Speech Processing.; Statistics, general.

 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 microarray data analysis; approaches to the global modeling and analysis of gene regulatory networks and transcriptional control, using methods, theories, and tools from signal processing, machine learning, information theory, and control theory; state-of-the-art tools in Boolean function theory, time-frequency analysis, pattern recognition, and unsupervised learning, applied to cancer classification, identification of biologically active sites, and visualization of gene expression data; crucial issues associated with statistical analysis of microarray data, statistics and stochastic analysis of gene expression levels in a single cell, statistically sound design of microarray studies and experiments; and biological and medical implications of genomics research. 

 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.