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Developing intuitive and explainable algorithms through inspiration from human physiology and computational biology.

Houcemeddine Turki1, Mohamed Ali Hadj Taieb2, Mohamed Ben Aouicha3

  • 1Biomedical Data Science and Scientometrics at the Data Engineering and Semantics Research Unit, University of Sfax, Tunisia. He is also a medical student at the Faculty of Medicine of Sfax, University of Sfax, Tunisia.

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This study introduces intuitive, explainable methods from human physiology and computational biology to improve knowledge resource processing and generation. These approaches aim to simplify and enhance how we create and utilize scientific information.

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Area of Science:

  • Computational Biology
  • Bioinformatics
  • Knowledge Management

Background:

  • Current knowledge resource processing can be complex and opaque.
  • There is a need for more intuitive and explainable methods in scientific information handling.

Purpose of the Study:

  • To present intuitive and explainable methods for knowledge resource processing.
  • To demonstrate how human physiology and computational biology can inform these methods.
  • To improve the simplification and amelioration of knowledge generation and processing.

Main Methods:

  • Leveraging principles from human physiology.
  • Applying computational biology techniques.
  • Developing intuitive and explainable frameworks for knowledge management.

Main Results:

  • Proposed methods simplify complex knowledge processing.
  • Enhanced explainability in knowledge generation.
  • Improved efficiency in creating and utilizing scientific resources.

Conclusions:

  • Intuitive and explainable methods offer significant advantages for knowledge resources.
  • Inspiration from biological systems can revolutionize scientific information processing.
  • These approaches promise to ameliorate the future of scientific knowledge management.