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Updated: Jan 31, 2026

A Protocol for Computer-Based Protein Structure and Function Prediction
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Kidney-inspired algorithm with reduced functionality treatment for classification and time series prediction.

Najmeh Sadat Jaddi1, Salwani Abdullah1

  • 1Data Mining and Optimization Research Group (DMO), Centre for Artificial Intelligence Technology, Faculty of Information Science and Technology, National University of Malaysia, Bangi, Selangor, Malaysia.

Plos One
|January 5, 2019
PubMed
Summary
This summary is machine-generated.

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This study introduces a novel kidney-inspired algorithm (KA) enhancement, using a glomerular filtration rate (GFR) test mimic to improve optimization. The modified KA demonstrates superior performance in various benchmark and real-world problems, enhancing search space exploration.

Area of Science:

  • Artificial Intelligence
  • Computational Biology
  • Optimization Algorithms

Background:

  • Artificial neural network (ANN) optimization commonly uses algorithms to find solutions for real-world problems.
  • The kidney-inspired algorithm (KA) mimics kidney functions: filtration, reabsorption, secretion, and excretion.
  • Reduced kidney function necessitates treatments, analogous to enhancing optimization algorithms for better performance.

Purpose of the Study:

  • To enhance the kidney-inspired algorithm (KA) by incorporating a glomerular filtration rate (GFR) test mimic.
  • To improve the exploration of the search space and the likelihood of finding optimal solutions.
  • To adapt the algorithm for both minimization and maximization problems.

Main Methods:

  • Mimicking the human kidney's glomerular filtration rate (GFR) test to guide algorithm steps.

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  • Implementing conditional actions based on GFR test results ( <15, 15-60, >60) to modify the basic KA process.
  • Testing the enhanced KA on benchmark functions, classification, time series prediction, and a water quality prediction problem.

    • Main Results:

    • The proposed GFR-enhanced KA demonstrated improved optimization outcomes compared to the basic KA.
    • The method showed competitive performance on benchmark classification and time series prediction tasks.
    • Application to a real-world water quality prediction problem confirmed the algorithm's effectiveness.

    Conclusions:

    • The GFR-mimicking approach effectively enhances the kidney-inspired algorithm's optimization capabilities.
    • The modified KA offers improved search space exploration and solution-finding likelihood.
    • This novel method shows promise for diverse optimization challenges, including real-world applications.