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<p>Sequence-based analysis and prediction form a cornerstone of bioinformatics investigations of the sequence-structure-function paradigm across DNA, RNA, and proteins. The exponential growth in sequence data necessitates sequence encoding methods and advanced predictive models. This study introduces a comprehensive machine learning and deep learning platform SeqHIVE, which aims to streamline the development of prediction pipelines for nucleic acid and protein sequences. SeqHIVE is implemented as a graphical user interface (GUI)–driven platform, enabling intuitive construction, evaluation, and visualization of sequence-based predictive models. SeqHIVE integrates a broad spectrum of 19 biological sequence encoding algorithms, 5 feature selection algorithms, and 26 classifiers for the biological sequence prediction tasks. It automates the processes of sequence-based feature extraction, model construction, predictive performance assessment, statistical analysis, and data visualization. SeqHIVE is engineered to serve both expert bioinformatics researchers with extensive customizable options, and biologists through a user-friendly interface and an intuitive design process. The utility of SeqHIVE is exemplified through a case study on iLearnPlus DNA locus prediction task. The tool SeqHIVE, the documentation, and its source code are freely available at: <a href="https://healthinformaticslab.org/supp/">https://healthinformaticslab.org/supp/</a>. and is also hosted on the GitHub repository: <a href="https://github.com/EienKune/SeqHIVE">https://github.com/EienKune/SeqHIVE</a>.</p>

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SeqHIVE: a Python package to convert the biological sequences to informative vectors for sequence property predictions

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年4月11日

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  • Xin Feng,
  • Cheng Gao,
  • Sudan Bai,
  • Jiaxin Zheng,
  • Cuinan Yu,
  • Kewei Li,
  • Lan Huang,
  • Bo Han,
  • Tao You,
  • Jun Zhang,
  • Fengfeng Zhou

摘要

Sequence-based analysis and prediction form a cornerstone of bioinformatics investigations of the sequence-structure-function paradigm across DNA, RNA, and proteins. The exponential growth in sequence data necessitates sequence encoding methods and advanced predictive models. This study introduces a comprehensive machine learning and deep learning platform SeqHIVE, which aims to streamline the development of prediction pipelines for nucleic acid and protein sequences. SeqHIVE is implemented as a graphical user interface (GUI)–driven platform, enabling intuitive construction, evaluation, and visualization of sequence-based predictive models. SeqHIVE integrates a broad spectrum of 19 biological sequence encoding algorithms, 5 feature selection algorithms, and 26 classifiers for the biological sequence prediction tasks. It automates the processes of sequence-based feature extraction, model construction, predictive performance assessment, statistical analysis, and data visualization. SeqHIVE is engineered to serve both expert bioinformatics researchers with extensive customizable options, and biologists through a user-friendly interface and an intuitive design process. The utility of SeqHIVE is exemplified through a case study on iLearnPlus DNA locus prediction task. The tool SeqHIVE, the documentation, and its source code are freely available at: https://healthinformaticslab.org/supp/. and is also hosted on the GitHub repository: https://github.com/EienKune/SeqHIVE.