By He Zengyou
Data Mining for Bioinformatics Applications presents useful info at the info mining tools were commonplace 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 solution to illustrate the way to follow facts mining strategies to unravel genuine bioinformatics difficulties, containing forty five bioinformatics difficulties which have been investigated in fresh examine. for every instance, the complete information mining strategy is defined, starting from facts preprocessing to modeling and outcome validation.
- Provides invaluable details at the information mining equipment were standard for fixing actual bioinformatics problems
- Uses an example-based way to illustrate easy methods to observe information mining concepts to unravel genuine bioinformatics problems
- Contains forty five bioinformatics difficulties which were investigated in fresh research
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Additional info for Data Mining for Bioinformatics Applications
Example text
This procedure is “almost unbiased” when random sampling is used in fold generation. However, this is not true with separate sampling, where the positive data and negative data are independently sampled [10]. It has been shown that the classical cross-validation can have strong bias under the separate sampling in Ref. [10]. Therefore, to use cross-validation with separate sampling in phosphorylation site prediction in the future, one should use the separate-sampling version of cross-validation in Ref.
This method assumes that buried residues would not be physically accessible to any kinase, thus improving the quality of negative training data. For both non-kinase-specific and kinase-specific predictions, the empirical comparison shows that different training data construction methods have different prediction performance and the difference is significant according to several statistical tests [1]. 2 Feature extraction To generate features for classifier training and testing, there are two widely adopted strategies in the literature.
All rights reserved. 2 Data Mining for Bioinformatics Applications Protein identification in proteomics In shotgun proteomics, the computational procedure for protein identification has two main steps: peptide identification and protein inference. In peptide identification, we search the experimental tandem mass spectra against a protein sequence database to obtain a set of peptide-spectrum matches, or use the de novo sequencing to determine the peptide sequences without using the protein database.