Knowledge discovery in databases: an overview
AI Magazine
From data mining to knowledge discovery: an overview
Advances in knowledge discovery and data mining
Systems for Knowledge Discovery in Databases
IEEE Transactions on Knowledge and Data Engineering
Knowledge Discovery in Molecular Databases
IEEE Transactions on Knowledge and Data Engineering
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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 novel 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 300 million sequences and over 2.5 billion characters of sequenced nucleotides. The focus of this paper is on developing a general data mining algorithm for discovering regions of locus control, i.e. those regions that are instrumental for activating genes. One type of such elements of locus control are the MARs or the Matrix Association Regions. Our limited knowledge about MARs has hampered their detection using classical pattern recognition techniques. Consequently, their detection is formulated by utilizing a statistical interestingness measure derived from a set of empirical features that are known to be associated with MARs. This paper presents a systematic approach for finding associations between such empirical features in genomic sequences, and for utilizing this knowledge in detecting biologically interesting control signals, such as MARs. This computational MAR discovery tool is implemented as a web-based software called MAR-Wiz and is available for public access. As our knowledge about the living system continues to evolve, and as the biological databases continue to grow, a pattern learning methodology similar to that described in this paper will be significant for the detection of regulatory signals embedded in genomic sequences.