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Data Mining for Bioinformatics Applications
Data Mining for Bioinformatics Applications
Knygos.lt klubas Knygos.lt nariams
361,68 €
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Data Mining for Bioinformatics Applications provides valuable information on the data mining methods have been widely used for solving real bioinformatics problems, including problem definition, data collection, data preprocessing, modeling, and validation. The text uses an example-based method to illustrate how to apply data mining techniques to solve real bioinformatics problems, containing 45 bioinformatics problems that have been investigated in recent research. For each example, the entir…
  • Leidėjas:
  • ISBN-10: 0081001002
  • ISBN-13: 9780081001004
  • Formatas: 15.2 x 22.9 x 0.8 cm, kieti viršeliai
  • Kalba: Anglų

Data Mining for Bioinformatics Applications (el. knyga) (skaityta knyga) | knygos.lt

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Data Mining for Bioinformatics Applications provides valuable information on the data mining methods have been widely used for solving real bioinformatics problems, including problem definition, data collection, data preprocessing, modeling, and validation.

The text uses an example-based method to illustrate how to apply data mining techniques to solve real bioinformatics problems, containing 45 bioinformatics problems that have been investigated in recent research. For each example, the entire data mining process is described, ranging from data preprocessing to modeling and result validation.

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  • Autorius: He Zengyou
  • Leidėjas:
  • ISBN-10: 0081001002
  • ISBN-13: 9780081001004
  • Formatas: 15.2 x 22.9 x 0.8 cm, kieti viršeliai
  • Kalba: Anglų

Data Mining for Bioinformatics Applications provides valuable information on the data mining methods have been widely used for solving real bioinformatics problems, including problem definition, data collection, data preprocessing, modeling, and validation.

The text uses an example-based method to illustrate how to apply data mining techniques to solve real bioinformatics problems, containing 45 bioinformatics problems that have been investigated in recent research. For each example, the entire data mining process is described, ranging from data preprocessing to modeling and result validation.

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