Call for Papers - Towards Overcoming Barriers of Bioinformatics Using Big Data Analytics


Bioinformatics is a discipline of data science that combines technology and biology. Numerous techniques and tools for storing, organising, comprehending, and deciphering the exponential quantity of biological information are generated in this discipline, with the ultimate goal of tackling problems in biology and medicine. Data volume increases everywhere, especially in bioinformatics research, as all processes are digitised and more affordable high-throughput machines. Technologies for collecting biological data, such as automated genome sequencers, are becoming more affordable and effective, ushering in a new age of big data in bioinformatics. Traditional methods of data analysis have grown outmoded and ineffective. Still, bioinformatics and advanced analytics appear to deliver a solution to this issue, as numerous systems are designed that can recognise large-scale trends and provide predictions, such as protein complexes and genomics.

In bioinformatics and computational biology, big data analytics (BDA) corresponds to various processes such as biological pattern recognition, categorisation, prediction, interpretation, clustering, and cloud-based data mining, among several others. Due to the large access of information-intensive data streams and developments in high computing technologies, BDA has emerged to do real-time predictive and descriptive analysis on huge amounts of data to devise intelligent, informed inferences. However, there is a lack of standards in big data frameworks and techniques for several crucial bioinformatics challenges, such as accelerated development of co-expression and signalling pathways and recognition of salient modules, identification of complexes over expanding protein interaction data, fast evaluation of gigantic DNA, RNA, and protein sequences, and quick query processing on iterative and multifaceted disease networks. Nevertheless, BDA in bioinformatics is still in its early stages, and concerns about data protection and standardisation must be overcome. Future big data analytics implementations must concentrate on improving high-end integrated solutions that can analyse huge amounts of biological data at a low cost and faster to speed up bioinformatics exploration.

In conclusion, this special issue describes the emerging advancement in technology in bioinformatics. It mentions the plethora of benefits of big data analytics in bioinformatics and computational biology. Though with the immense potential of BDA in bioinformatics, this discipline is also faced with several challenges. Researchers and practitioners are welcome to contribute innovative solutions for the same.

Topics of interest include, but are not limited to:

  • The emergence of innovative technologies in computational biology
  • Big data and bioinformatics: challenges and opportunities
  • Recent trends and future research directions in big data approaches in bioinformatics
  • Role of big data in transforming the bioinformatics field
  • BDA as an integrated approach for massive biological data
  • Challenges in the deployment of BDA in bioinformatics and computational biology
  • A new perspective on Machine learning techniques for computational biology
  • New frontiers in bioinformatics: Challenges and opportunities
  • Contribution of big data analytics in genome sequencing
  • Applications of BDA in bioinformatics in the digitised era
  • Deep learning models to overcome existing challenges in bioinformatics
  • Computational biology and bioinformatics: Existing challenges and solutions


Tentative schedule:
First submission deadline: September 25, 2023
Notification of first round decision: November 25, 2023
Revised paper submission deadline: January 25, 2024
Notification of final decision: February 25, 2024
Final paper submission deadline: March 30, 2024

Guest Editors:
Dr. Muhammad Sulaiman, Department of Mathematics, Abdul Wali Khan University, Mardan, Khyber Pakhtunkhwa, Pakistan
Dr. Maharani Abu Bakar, Special Interest Group Modelling and Data Analytics, Faculty of Ocean Engineering Technology and Informatics, Universiti Malaysia Terengganu, Terengganu, Malaysia
Dr. Zardad Khan, Department of Analytics in the Digital Era, United Arab Emirates University, Al Ain, United Arab Emirates