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Discovering Matrix Attachment Regions (MARs) in genomic databases

Discovering Matrix Attachment Regions (MARs) in genomic databases Discovering Matrix Attachment Regions (MARs) in Genomic Databases Gautam B. Singh Department of Computer Science & Engineering Oakland University Rochester, MI 48309 singh @ oakland.edu ABSTRACT Lately, there has been considerable interest in applying Data Mining techniques to scientific and data analysis problems in bioinformatics. Data mining research is being fueled by novel application areas that are helping the development of newer applied algorithms in the field of bioinformatics, an emerging discipline representing the integration of biological and information sciences. This is a shift in paradigm from the earlier and the continuing data mining efforts in marketing research and support for business intelligence. The problem described in this paper is along a new dimension in DNA sequence analysis research and supplements the previously studied stochastic models for evolution and variability. The discovery of now~l patterns from genetic databases as described is quite significant because biological pattern play an important role in a large variety of cellular processes and constitute the basis for gene therapy. Biological databases containing the genetic codes from a wide variety of organisms, including humans, have continued their exponential growth over the last decade. At the time of this writing, the GenBank database contains over http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png ACM SIGKDD Explorations Newsletter Association for Computing Machinery

Discovering Matrix Attachment Regions (MARs) in genomic databases

Singh, Gautam B.
ACM SIGKDD Explorations Newsletter , Volume 1 (2) – Jan 1, 2000
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Publisher
Association for Computing Machinery
Copyright
Copyright © 2000 by ACM Inc.
ISSN
1931-0145
DOI
10.1145/846183.846184
Publisher site
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Abstract

Discovering Matrix Attachment Regions (MARs) in Genomic Databases Gautam B. Singh Department of Computer Science & Engineering Oakland University Rochester, MI 48309 singh @ oakland.edu ABSTRACT Lately, there has been considerable interest in applying Data Mining techniques to scientific and data analysis problems in bioinformatics. Data mining research is being fueled by novel application areas that are helping the development of newer applied algorithms in the field of bioinformatics, an emerging discipline representing the integration of biological and information sciences. This is a shift in paradigm from the earlier and the continuing data mining efforts in marketing research and support for business intelligence. The problem described in this paper is along a new dimension in DNA sequence analysis research and supplements the previously studied stochastic models for evolution and variability. The discovery of now~l patterns from genetic databases as described is quite significant because biological pattern play an important role in a large variety of cellular processes and constitute the basis for gene therapy. Biological databases containing the genetic codes from a wide variety of organisms, including humans, have continued their exponential growth over the last decade. At the time of this writing, the GenBank database contains over

Journal

ACM SIGKDD Explorations Newsletter – Association for Computing Machinery

Published: Jan 1, 2000

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