E-Book, Englisch, 335 Seiten
An Algorithmic Approach
E-Book, Englisch, 335 Seiten
Reihe: Chapman & Hall/CRC Computer Science & Data Analysis
ISBN: 978-1-4200-9154-0
Verlag: Taylor & Francis
Format: PDF
Kopierschutz: Adobe DRM (»Systemvoraussetzungen)
The book first explores a new technique that takes advantage of a multiview approach to image analysis and addresses the challenges of applying powerful traditional techniques, such as clustering, to full-scale microarray experiments. It then presents an effective feature identification approach, an innovative technique that renders highly detailed surface models, a new approach to subgrid detection, a novel technique for the background removal process, and a useful technique for removing "noise." The authors also develop an expectation–maximization (EM) algorithm for modeling gene regulatory networks from gene expression time series data. The final chapter describes the overall benefits of these techniques in the biological and computer sciences and reviews future research topics.
This book systematically brings together the fields of image processing, data analysis, and molecular biology to advance the state of the art in this important area. Although the text focuses on improving the processes involved in the analysis of microarray image data, the methods discussed can be applied to a broad range of medical and computer vision analysis areas.
Zielgruppe
Researchers and graduate students in bioinformatics, computer science, and statistics.
Autoren/Hrsg.
Weitere Infos & Material
Introduction
Overview
Current state of art
Experimental approach
Key issues
Contribution to knowledge
Structure of the book
Background
Introduction
Molecular biology
Microarray technology
Microarray analysis
Copasetic microarray analysis framework overview
Summary
Data Services
Introduction
Image transformation engine
Evaluation
Summary
Structure Extrapolation I
Introduction
Pyramidic contextual clustering
Evaluation
Summary
Structure Extrapolation II
Introduction
Image layout—master blocks
Image structure—meta-blocks
Summary
Feature Identification I
Introduction
Spatial binding
Evaluation of feature identification
Evaluation of copasetic microarray analysis framework
Summary
Feature Identification II
Background
Proposed approach—subgrid detection
Experimental results
Conclusions
Chained Fourier Background Reconstruction
Introduction
Existing techniques
A new technique
Experiments and results
Conclusions
Graph-Cutting for Improving Microarray Gene Expression
Reconstructions
Introduction
Existing techniques
Proposed technique
Experiments and results
Conclusions
Stochastic Dynamic Modeling of Short Gene Expression Time Series Data
Introduction
Stochastic dynamic model for gene expression data
An EM algorithm for parameter identification
Simulation results
Discussions
Conclusions and future work
Conclusions
Introduction
Achievements
Contributions to microarray biology domain
Contributions to computer science domain
Future research topics
Appendix A: Microarray Variants
Appendix B: Basic Transformations
Appendix C: Clustering
Appendix D: A Glance on Mining Gene Expression Data
Appendix E: Autocorrelation and GHT
References
