A Comprehensive Survey of Genetic Programming Applications in Modern Biological Research

Abstract

Genetic programming (GP) has emerged as a powerful tool over the past two decades, leveraging evolutionary algorithms to navigate high-dimensional solution spaces effectively. This paper provides a comprehensive survey of GP's applications across various scientific domains, with a particular focus on bioinformatics and drug discovery. We discuss how GP facilitates the quantification, localization, and functional analysis of proteins. We highlighted its role in improving mass spectrometric (MS) peptide detectability through advanced preprocessing techniques. By enhancing the identification accuracy of peptides in proteomics, GP has significantly surpassed traditional methods. Additionally, we explore GP's capabilities in pattern matching and motif discovery within protein and DNA sequences, underscoring its utility in cancer research and biomarker detection. The paper also examines the integration of GP with machine learning strategies to address challenges in mass spectrometry, enabling the identification of biomarkers from complex datasets. Furthermore, we discuss innovative GP-based methods for predicting protein structure and function, as well as its application in drug discovery, where it outperforms conventional machine learning techniques in predicting pharmacokinetic properties. Through this survey, we aim to elucidate the versatility and effectiveness of genetic programming in tackling complex biological problems, paving the way for future research and applications in the life sciences.

Received: 25 June 2024 | Revised: 8 October 2024 | Accepted: 12 December 2024

Conflicts of Interest

The author declares that he has no conflicts of interest to this work.

Data Availability Statement

Data sharing is not applicable to this article as no new data were created or analyzed in this study.

Author Contribution Statement

Mohammad Wahab Khan: Conceptualization, Methodology, Formal analysis, Investigation, Resources, Data curation, Writing - original draft, Writing - review & editing, Visualization, Supervision, Project administration.