To harness the high-throughput potential of DNA microarray
technology, it is crucial that the analysis stages of the process
are decoupled from the requirements of operator assistance.
Microarray Image Analysis An Algorithmic Approach presents an
automatic system for microarray image processing to make this
decoupling a reality. The proposed system integrates and extends
traditional analytical-based methods and custom-designed novel
algorithms.
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.
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