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Data mining of environmental stress tolerances on plants.

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Title: Data mining of environmental stress tolerances on plants.
Authors: Richard S. Segall, Gauri S. Guha, Sarath A. Nonis
Source: Kybernetes. Jan2008, Vol. 37 Issue 1, p127-148. 22p.
Subjects: Data mining, Databases, Osmotic potential of plants, Cluster analysis (Statistics), Medical botany
Abstract: Purpose - This paper seeks to present a complete set of graphical and numerical outputs of data mining performed for microarray databases of plant data as described in earlier research by the authors. A brief description of data mining is also presented, as well as a brief background of previous research. Design/methodology/approach - The paper uses applications of data mining using SAS Enterprise Miner Version 4 for plant data from the Osmotic Stress Microarray Information Database (OSMID) that is available on the web for both normalized and log(2) transformed data. Findings - This paper illustrates that useful information about the effects of environmental stress tolerances (ESTs) on plants can be obtained by using data mining. Research limitations/implications - Use of SAS Enterprise Miner was very effective for performing data mining of microarray databases with its modules of cluster analysis, decision trees, and descriptive and visual statistics. Practical implications - The data used from the OSMID database are considered to be representative of those that could be used for biotech application such as the manufacture of plant-made-pharmaceuticals and genetically modified foods. Originality/value - This paper contributes to the discussion on the use of data mining for microarray databases and specifically for studying the effects of ESTs on plants. [ABSTRACT FROM AUTHOR]
Copyright of Kybernetes is the property of Emerald Publishing Limited and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Database: Engineering Source
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  Availability: 0
Header DbId: egs
DbLabel: Engineering Source
An: 31398674
AccessLevel: 6
PubType: Periodical
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  Data: Data mining of environmental stress tolerances on plants.
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  Data: <searchLink fieldCode="AR" term="%22Richard+S%2E+Segall%22">Richard S. Segall</searchLink><br /><searchLink fieldCode="AR" term="%22Gauri+S%2E+Guha%22">Gauri S. Guha</searchLink><br /><searchLink fieldCode="AR" term="%22Sarath+A%2E+Nonis%22">Sarath A. Nonis</searchLink>
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  Data: <searchLink fieldCode="JN" term="%22Kybernetes%22">Kybernetes</searchLink>. Jan2008, Vol. 37 Issue 1, p127-148. 22p.
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  Data: <searchLink fieldCode="DE" term="%22Data+mining%22">Data mining</searchLink><br /><searchLink fieldCode="DE" term="%22Databases%22">Databases</searchLink><br /><searchLink fieldCode="DE" term="%22Osmotic+potential+of+plants%22">Osmotic potential of plants</searchLink><br /><searchLink fieldCode="DE" term="%22Cluster+analysis+%28Statistics%29%22">Cluster analysis (Statistics)</searchLink><br /><searchLink fieldCode="DE" term="%22Medical+botany%22">Medical botany</searchLink>
– Name: Abstract
  Label: Abstract
  Group: Ab
  Data: Purpose - This paper seeks to present a complete set of graphical and numerical outputs of data mining performed for microarray databases of plant data as described in earlier research by the authors. A brief description of data mining is also presented, as well as a brief background of previous research. Design/methodology/approach - The paper uses applications of data mining using SAS Enterprise Miner Version 4 for plant data from the Osmotic Stress Microarray Information Database (OSMID) that is available on the web for both normalized and log(2) transformed data. Findings - This paper illustrates that useful information about the effects of environmental stress tolerances (ESTs) on plants can be obtained by using data mining. Research limitations/implications - Use of SAS Enterprise Miner was very effective for performing data mining of microarray databases with its modules of cluster analysis, decision trees, and descriptive and visual statistics. Practical implications - The data used from the OSMID database are considered to be representative of those that could be used for biotech application such as the manufacture of plant-made-pharmaceuticals and genetically modified foods. Originality/value - This paper contributes to the discussion on the use of data mining for microarray databases and specifically for studying the effects of ESTs on plants. [ABSTRACT FROM AUTHOR]
– Name: AbstractSuppliedCopyright
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  Group: Ab
  Data: <i>Copyright of Kybernetes is the property of Emerald Publishing Limited and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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      – Type: doi
        Value: 10.1108/03684920810851041
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      – Code: eng
        Text: English
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        PageCount: 22
        StartPage: 127
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      – SubjectFull: Data mining
        Type: general
      – SubjectFull: Databases
        Type: general
      – SubjectFull: Osmotic potential of plants
        Type: general
      – SubjectFull: Cluster analysis (Statistics)
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      – SubjectFull: Medical botany
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      – TitleFull: Data mining of environmental stress tolerances on plants.
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              Text: Jan2008
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              Y: 2008
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