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...

Full description

Bibliographic Details
Corporate Author: SpringerLink (Online service)
Other Authors: Zhang, Wei. (Editor), Shmulevich, Ilya. (Editor)
Format: Electronic
Language:English
Published: Boston, MA : Springer US, 2002.
Subjects:
Online Access:View fulltext via EzAccess
LEADER 04052nam a22005175i 4500
001 23093
003 DE-He213
005 20151204174643.0
007 cr nn 008mamaa
008 100301s2002 xxu| s |||| 0|eng d
020 # # |a 9780306478253  |9 978-0-306-47825-3 
024 7 # |a 10.1007/b101927  |2 doi 
050 # 4 |a QL801-950.9 
072 # 7 |a PSV  |2 bicssc 
072 # 7 |a SCI070000  |2 bisacsh 
072 # 7 |a SCI056000  |2 bisacsh 
082 0 4 |a 571.31  |2 23 
245 1 0 |a Computational and Statistical Approaches to Genomics  |c edited by Wei Zhang, Ilya Shmulevich.  |h [electronic resource] / 
264 # 1 |a Boston, MA :  |b Springer US,  |c 2002. 
300 # # |a XIV, 329 p. 88 illus., 20 illus. in color.  |b online resource. 
336 # # |a text  |b txt  |2 rdacontent 
337 # # |a computer  |b c  |2 rdamedia 
338 # # |a online resource  |b cr  |2 rdacarrier 
347 # # |a text file  |b PDF  |2 rda 
505 0 # |a 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. 
520 # # |a 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. 
650 # 0 |a Life sciences. 
650 # 0 |a Cancer research. 
650 # 0 |a Biotechnology. 
650 # 0 |a Animal anatomy. 
650 # 0 |a Statistics. 
650 1 4 |a Life Sciences. 
650 2 4 |a Animal Anatomy / Morphology / Histology. 
650 2 4 |a Cancer Research. 
650 2 4 |a Biotechnology. 
650 2 4 |a Signal, Image and Speech Processing. 
650 2 4 |a Statistics, general. 
700 1 # |a Zhang, Wei.  |e editor. 
700 1 # |a Shmulevich, Ilya.  |e editor. 
710 2 # |a SpringerLink (Online service) 
773 0 # |t Springer eBooks 
776 0 8 |i Printed edition:  |z 9781402070235 
856 4 0 |u https://ezaccess.library.uitm.edu.my/login?url=http://dx.doi.org/10.1007/b101927  |z View fulltext via EzAccess 
912 # # |a ZDB-2-SBL 
912 # # |a ZDB-2-BAE 
950 # # |a Biomedical and Life Sciences (Springer-11642)