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Using data mining techniques to learn layouts of flat-file biological datasets

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4 Author(s)
Sinha, K. ; Dept. of Comput. Sci. & Eng., Ohio State Univ., Columbus, OH, USA ; Xuan Zhang ; Ruoming Jin ; Agrawal, G.

One of the major problems in biological data integration is that many data sources are stored as atlasses, with a variety of different layouts. Integrating data from such sources can be an extremely time-consuming task. We have been developing data mining techniques to help learn the layout of a dataset in a semi-automatic way. In this paper, we focus on the problem of identifying delimiters for optional fields. Since these fields do not occur in every record, frequency based methods are not able to identify the corresponding delimiters. We present a method which uses contrast analysis on the frequency of sequences to identify such delimiters and help complete the layout descriptions. We demonstrate the effectiveness of this technique using three atlasses biological datasets.

Published in:

Bioinformatics and Bioengineering, 2005. BIBE 2005. Fifth IEEE Symposium on

Date of Conference:

19-21 Oct. 2005

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