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  • PyFeat: A Python-based Effective Feature Generation Tool for DNA, RNA, and Protein Sequences.

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    Accepted Manuscript (AM)
    Author(s)
    Muhammod, Rafsanjani
    Ahmed, Sajid
    Farid, Dewan Md
    Shatabda, Swakkhar
    Sharma, Alok
    Dehzangi, Abdollah
    Griffith University Author(s)
    Sharma, Alok
    Year published
    2019
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    Abstract
    MOTIVATION: Extracting useful feature set which contains significant discriminatory information is a critical step in effectively presenting sequence data to predict structural, functional, interaction and expression of proteins, DNAs, and RNAs. Also, being able to filter features with significant information and avoid sparsity in the extracted features require the employment of efficient feature selection techniques. Here we present PyFeat as a practical and easy to use toolkit implemented in Python for extracting various features from proteins, DNAs, and RNAs. To build PyFeat we mainly focused on extracting features that ...
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    MOTIVATION: Extracting useful feature set which contains significant discriminatory information is a critical step in effectively presenting sequence data to predict structural, functional, interaction and expression of proteins, DNAs, and RNAs. Also, being able to filter features with significant information and avoid sparsity in the extracted features require the employment of efficient feature selection techniques. Here we present PyFeat as a practical and easy to use toolkit implemented in Python for extracting various features from proteins, DNAs, and RNAs. To build PyFeat we mainly focused on extracting features that capture information about the interaction of neighboring residues to be able to provide more local information. We then employ AdaBoost technique to select features with maximum discriminatory information. In this way, we can significantly reduce the number of extracted features and enable PyFeat to represent the combination of effective features from large neighboring residues. As a result, PyFeat is able to extract features from 13 different techniques and represent context free combination of effective features. The source code for PyFeat standalone toolkit and employed benchmarks with a comprehensive user manual explaining its system and workflow in a step by step manner are publicly available. RESULTS: https://github.com/mrzResearchArena/PyFeat/blob/master/RESULTS.md. AVAILABILITY: Toolkit, source code, and manual to use PyFeat: https://github.com/mrzResearchArena/PyFeat/.
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    Journal Title
    Bioinformatics
    DOI
    https://doi.org/10.1093/bioinformatics/btz165
    Copyright Statement
    © 2019 Oxford University Press. This is a pre-copy-editing, author-produced PDF of an article accepted for publication in Bioinformatics following peer review. The definitive publisher-authenticated version PyFeat: a Python-based effective feature generation tool for DNA, RNA and protein sequences, Bioinformatics, is available online at: https://doi.org/10.1093/bioinformatics/btz165.
    Note
    This publication has been entered into Griffith Research Online as an Advanced Online Version.
    Subject
    Mathematical sciences
    Biological sciences
    Publication URI
    http://hdl.handle.net/10072/386354
    Collection
    • Journal articles

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