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Machine learning and data mining techniques have become indispensable in the field of bioinformatics with the advancements in big data analysis such as omics, biomedical imaging, and signal processing. Toward developing a pedagogical solution for data preprocessing, this paper explores design architecture of easily accessible, free-cost novel open-source web-based virtual laboratory to simplify the data preprocessing and visualization steps for big data analysis. A user-friendly interface combines computational capabilities with HTML and JavaScript as the underlying technology. This virtual laboratory architecture was designed to be compatible for access using mobile phones, tablets, and desktop computers having an active internet connection, making the education resource usage independent of end user dependencies such as specific operating systems, installed apps or other not so easily available applications. The software implementation was created to allow student learners to preprocess and classify data, incorporating techniques like variable type conversion, normalization, standardization. With this web-based virtual laboratory, large datasets can also be preprocessed and classified that may help as education resources for students, data analysts and researchers from different domains.

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Developing and Deploying Open-Access Web-Based Educational Tools for Teaching Data Preprocessing and Classification in Bioinformatics

  • 会议论文

  • 发表日期：

    2025年

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  • Bhavya Ottappurakkal,
  • Dhanush Kumar,
  • Asha Vijayan,
  • Rakhi Radhamani,
  • Krishnashree Achuthan,
  • Shyam Diwakar

摘要

Machine learning and data mining techniques have become indispensable in the field of bioinformatics with the advancements in big data analysis such as omics, biomedical imaging, and signal processing. Toward developing a pedagogical solution for data preprocessing, this paper explores design architecture of easily accessible, free-cost novel open-source web-based virtual laboratory to simplify the data preprocessing and visualization steps for big data analysis. A user-friendly interface combines computational capabilities with HTML and JavaScript as the underlying technology. This virtual laboratory architecture was designed to be compatible for access using mobile phones, tablets, and desktop computers having an active internet connection, making the education resource usage independent of end user dependencies such as specific operating systems, installed apps or other not so easily available applications. The software implementation was created to allow student learners to preprocess and classify data, incorporating techniques like variable type conversion, normalization, standardization. With this web-based virtual laboratory, large datasets can also be preprocessed and classified that may help as education resources for students, data analysts and researchers from different domains.