A next-generation dynamic programming language Julia: Its features and applications in biological science

Soumen Pal; Manojit Bhattacharya; Snehasish Dash; Sang-Soo Lee; Chiranjib Chakraborty

doi:10.1016/j.jare.2023.11.015

A next-generation dynamic programming language Julia: Its features and applications in biological science

J Adv Res. 2023 Nov 21:S2090-1232(23)00352-1. doi: 10.1016/j.jare.2023.11.015. Online ahead of print.

Authors

Soumen Pal¹, Manojit Bhattacharya², Snehasish Dash¹, Sang-Soo Lee³, Chiranjib Chakraborty⁴

Affiliations

¹ School of Mechanical Engineering, Vellore Institute of Technology, Vellore 632014, Tamil Nadu, India.
² Department of Zoology, Fakir Mohan University, Vyasa Vihar, Balasore 756020, Odisha, India.
³ Institute for Skeletal Aging & Orthopedic Surgery, Hallym University-Chuncheon Sacred Heart Hospital, Chuncheon, Gangwon-Do 24252, Republic of Korea. Electronic address: 123sslee@gmail.com.
⁴ Department of Biotechnology, School of Life Science and Biotechnology, Adamas University, Kolkata, West Bengal 700126, India. Electronic address: drchiranjib@yahoo.com.

PMID: 37992995
DOI: 10.1016/j.jare.2023.11.015

Abstract

Background: The advent of Julia as a sophisticated and dynamic programming language in 2012 represented a significant milestone in computational programming, mathematical analysis, and statistical modeling. Having reached its stable release in version 1.9.0 on May 7, 2023, Julia has developed into a powerful and versatile instrument. Despite its potential and widespread adoption across various scientific and technical domains, there exists a noticeable knowledge gap in comprehending its utilization within biological sciences.

The aim of review: This comprehensive review aims to address this particular knowledge gap and offer a thorough examination of Julia's fundamental characteristics and its applications in biology.

Key scientific concepts of the review: The review focuses on a research gap in the biological science. The review aims to equip researchers with knowledge and tools to utilize Julia's capabilities in biological science effectively and to demonstrate the gap. It paves the way for innovative solutions and discoveries in this rapidly evolving field. It encompasses an analysis of Julia's characteristics, packages, and performance compared to the other programming languages in this field. The initial part of this review discusses the key features of Julia, such as its dynamic and interactive nature, fast processing speed, ease of expression manipulation, user-friendly syntax, code readability, strong support for multiple dispatch, and advanced type system. It also explores Julia's capabilities in data analysis, visualization, machine learning, and algorithms, making it suitable for scientific applications. The next section emphasizes the importance of using Julia in biological research, highlighting its seamless integration with biological studies for data analysis, and computational biology. It also compares Julia with other programming languages commonly used in biological research through benchmarking and performance analysis. Additionally, it provides insights into future directions and potential challenges in Julia's applications in biology.

Keywords: Biological science; Computational biology; Julia; Programming language.

Publication types

Review