Vol. 5 No. 1 (2025): Issue 5
Articles

Logistic Regression-based Approach for Gene Function Prediction

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  • Wei-Ling Chen,
  • Huang Jin-Tai,
  • Lin Mei-Yu
Wei-Ling Chen
Department of Bioinformatics, National University of Tainan, Tainan, 70101, Taiwan
Huang Jin-Tai
Institute of Computational Biology, National Chiayi University, Chiayi City, 60004, Taiwan
Lin Mei-Yu
Center for Genomic Medicine, National Ilan University, Yilan, 26047, Taiwan
DOI: https://doi.org/10.71070/oaml.v5i1.90

Published 2025-02-27

Keywords

  • Gene Function Prediction,
  • Bioinformatics,
  • Computational Methods,
  • Logistic Regression,
  • Gene Expression Data

How to Cite

Chen, W.-L., Jin-Tai, H., & Mei-Yu, L. (2025). Logistic Regression-based Approach for Gene Function Prediction. Optimizations in Applied Machine Learning, 5(1). https://doi.org/10.71070/oaml.v5i1.90
Creative Commons License

This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.

Abstract

Gene function prediction is a critical task in bioinformatics, as it provides insights into the roles and interactions of genes within biological systems. The current research landscape is characterized by a variety of computational methods aimed at improving prediction accuracy. However, existing approaches often face challenges related to scalability and interpretability. In this paper, we propose a novel logistic regression-based approach that leverages gene expression data to predict gene functions more effectively. By incorporating expression profiles into the prediction model, our method offers improved accuracy and interpretability compared to traditional methods. Our experimental results demonstrate the efficacy of the proposed approach in accurately predicting gene functions, thus highlighting its potential to enhance our understanding of complex biological systems.
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