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PyFeat: a python - based effective feature generation tool for DNA, RNA, and protein sequences

Muhammod, Rafsanjani and Ahmed, Sajid and Farid, Dewan M. and Shatabda, Swakkhar and Sharma, Alokanand and Dehzangi, Abdollah (2019) PyFeat: a python - based effective feature generation tool for DNA, RNA, and protein sequences. Bioinformatics, 165 . pp. 1-2. ISSN 1367-4803

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    Abstract

    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.

    Item Type: Journal Article
    Subjects: Q Science > Q Science (General)
    T Technology > T Technology (General)
    Divisions: Faculty of Science, Technology and Environment (FSTE) > School of Engineering and Physics
    Depositing User: Alokanand Sharma
    Date Deposited: 02 Apr 2019 12:31
    Last Modified: 02 Apr 2019 12:31
    URI: http://repository.usp.ac.fj/id/eprint/11402
    UNSPECIFIED

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