By He Zengyou
Data Mining for Bioinformatics Applications presents necessary info at the information mining tools were favourite for fixing genuine bioinformatics difficulties, together with challenge definition, facts assortment, info preprocessing, modeling, and validation.
The textual content makes use of an example-based strategy to illustrate find out how to follow information mining strategies to resolve actual bioinformatics difficulties, containing forty five bioinformatics difficulties which have been investigated in contemporary study. for every instance, the whole facts mining approach is defined, starting from information preprocessing to modeling and end result validation.
- Provides beneficial details at the info mining tools were time-honored for fixing actual bioinformatics problems
- Uses an example-based technique to illustrate find out how to observe info mining ideas 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
In this example, there are 10 phosphorylated peptides in the foreground data P and 10 unphosphorylated peptides in the background data N, respectively. Each peptide has 13 amino acids and its central position is the phosphorylation site. In this example data set, we can observe that “KMS” is an interesting phosphorylation motif because it appears five times in P but never occurs in N. S” is not a meaningful phosphorylation motif because its appearance frequencies in P and N are equal. Data Mining for Bioinformatics Applications.
1 presents an example of multiple sequence alignment, where five biological sequences are aligned together. Motif discovery: Motif discovery is an important bioinformatics problem with numerous applications. Generally, a sequence motif is a nucleotide or amino-acid sequence pattern that is widespread and has a biological significance. Given a set of biological sequences, the motif discovery is to find a set of motifs, where each motif satisfies the given criteria. There are different problem formulations for motif discovery in different domains, ranging from regulatory DNA motif to posttranslational modification (PTM) motif of proteins.
In this regard, an alternative choice is to Phosphorylation motif discovery 27 generate simulated data with known ground truth. However, there is still no widely accepted simulation procedure for producing such synthetic data for performance evaluation. Thus, it is highly necessary to design a good simulator for this purpose. References  Y. , A summary of computational resources for protein phosphorylation, Curr. Protein Pept. Sci. 11 (2010) 485–496.  Z. He, C. Yang, G. , Motif-all: discovering all phosphorylation motifs, BMC Bioinf.