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Logistic Regression-based Approach for Gene Function Prediction

Year: 2025

Type: article

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.

Source: Optimizations in Applied Machine Learning

DOI: 10.71070/oaml.v5i1.90

Authors: Wei-Ling Chen, Huang Jin-Tai, Lin Mei-Yu

Institutions: National University of Tainan, National Chiayi University, National Ilan University

Language: English

Topic: Bioinformatics and Genomic Networks

Subfield: Molecular Biology

Field: Biochemistry, Genetics and Molecular Biology

Domain: Life Sciences

Open Access status: hybrid

OpenAlex ID: W4408117744

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