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Novel Sequence Discovery by Subtractive Genomics
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Mining Contiguous Sequential Generators in Biological Sequences.

Jingsong Zhang, Yinglin Wang, Chao Zhang

    IEEE/ACM Transactions on Computational Biology and Bioinformatics
    |November 4, 2015
    PubMed
    Summary
    This summary is machine-generated.

    This study introduces ConSgen, an efficient algorithm for discovering contiguous sequential generators in biological data. ConSgen overcomes limitations of previous methods, offering a more concise and effective way to analyze big data in bioinformatics.

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    Area of Science:

    • Bioinformatics
    • Computational Biology
    • Data Mining

    Background:

    • Discovering conserved sequential patterns in biological sequences is crucial for understanding shared functions.
    • Mining sequential generators and closed sequential patterns offers more concise results than mining all patterns, especially for big data in bioinformatics.
    • Previous research favors generators over closed patterns for inductive inference and classification.

    Purpose of the Study:

    • To address the challenge of inefficient and redundant patterns generated by classic sequential generator mining algorithms.
    • To propose an efficient algorithm, ConSgen, for discovering contiguous sequential generators, driven by applications requiring contiguous features.
    • To improve the effectiveness and usability of pattern mining in large-scale biological sequence analysis.

    Main Methods:

    • ConSgen utilizes the n-gram model (shingles) to generate potential frequent subsequences.
    • Employs several pruning techniques to reduce the search space by eliminating unpromising patterns.
    • Identifies contiguous sequential generators using an equivalence class-based lower-closure checking scheme.

    Main Results:

    • ConSgen demonstrates compactness in pattern discovery, generating a concise set of results.
    • The algorithm exhibits high efficiency in discovering contiguous sequential generators.
    • Experiments on DNA and protein datasets confirm the scalability of ConSgen.

    Conclusions:

    • ConSgen effectively addresses the limitations of traditional sequential pattern mining algorithms.
    • The proposed algorithm provides a compact, efficient, and scalable solution for discovering contiguous sequential generators in bioinformatics.
    • ConSgen enhances the analysis of big biological data by yielding more usable and relevant patterns.