Data Mining for Bioinformatics Applications by He Zengyou

By He Zengyou

Data Mining for Bioinformatics Applications offers precious details at the information mining equipment were regularly occurring for fixing actual bioinformatics difficulties, together with challenge definition, info assortment, information preprocessing, modeling, and validation.

The textual content makes use of an example-based approach to illustrate find out how to practice facts mining strategies to unravel actual bioinformatics difficulties, containing forty five bioinformatics difficulties which were investigated in fresh study. for every instance, the complete information mining strategy is defined, starting from information preprocessing to modeling and end result validation.

  • Provides worthy info at the information mining equipment were primary for fixing actual bioinformatics problems
  • Uses an example-based technique to illustrate the right way to practice facts mining recommendations to unravel genuine bioinformatics problems
  • Contains forty five bioinformatics difficulties which were investigated in fresh research

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Extra info for Data Mining for Bioinformatics Applications

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It has been demonstrated that the resultant predictor outperforms both the Arabidopsis-specific tools and a simpler machine-learning technique that uses only known phosphorylation sites from soybean. 4 Validation: Cross-validation and independent test Cross-validation and independent test are widely used for evaluating the classification performance in the context of both non-kinase-specific and kinase-specific phosphorylation site prediction. Cross-validation divides the training data into several disjointed parts of approximately equal size.

Network integration is to integrate networks of different types from the same species to gain a more comprehensive understanding on the overall biological system under study. The integration is achieved by merging different network types into a single network with multiple types of interactions over the same set of elements. Network querying searches a network to find subnetworks that are similar to a given subnetwork. 2 Network inference It is often impossible or expensive to determine the network structure by experimental validation of all interaction pairs between biological units.

The combination of precursor m/z and its tandem mass spectrum is used to determine peptide sequences, and then proteins are inferred from the identified peptides. Finally, peptides and proteins are quantified (either relatively or absolutely) to generate protein abundance. These protein abundances are then interpreted and further used for biomarker discovery or protein–protein interaction network construction. Data Mining for Bioinformatics Applications. 00005-3 © 2015 Elsevier Ltd. All rights reserved.

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