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Discriminative pattern mining and its applications in bioinformatics.

Xiaoqing Liu, Jun Wu, Feiyang Gu

    Briefings in Bioinformatics
    |December 1, 2014
    PubMed
    Summary

    Discriminative pattern mining identifies key patterns in data for group differences and classification. These techniques are highly valuable in bioinformatics for applications like gene expression and motif discovery.

    Keywords:
    contrast setsdiscriminative pattern miningemerging patternssubgroup discovery

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    Area of Science:

    • Data Mining
    • Bioinformatics
    • Computational Biology

    Background:

    • Discriminative pattern mining is crucial for identifying patterns with disproportionate frequencies across different data classes.
    • These patterns are valuable for detecting group differences and constructing classifiers.
    • The field is rapidly evolving with numerous algorithms proposed for this task.

    Purpose of the Study:

    • To provide an overview of discriminative pattern mining techniques.
    • To highlight effective methods for discriminative pattern discovery.
    • To illustrate the application of these methods in bioinformatics.

    Main Methods:

    • Overview of established and novel discriminative pattern mining algorithms.
    • Review of techniques for pattern discovery in class-labeled data.
    • Case studies demonstrating bioinformatics applications.

    Main Results:

    • Discriminative pattern mining offers significant value in biological data analysis.
    • Applications include phosphorylation motif discovery, differentially expressed gene identification, and genotype pattern detection.
    • Effective methods exist for tackling these bioinformatics challenges.

    Conclusions:

    • Discriminative pattern mining is a powerful tool for bioinformatics.
    • The discussed methods effectively address key biological data analysis problems.
    • Future work should focus on addressing remaining challenges in the field.