Recent Advancements in Computational Biology and Bioinformatics

TOPICAL COLLECTIONS

Guest Editors

Syed Nisar Hussain Bukhari

National Institute of Electronics & Information Technology (NIELIT), Srinagar, India

Syed Nisar Hussain Bukhari is a Scientist-D at the National Institute of Electronics and Information Technology (NIELIT), Srinagar, under the Ministry of Electronics and Information Technology (MeitY), Government of India. With over 12 years of teaching and research experience, he holds a PhD in Machine Learning and Immunoinformatics from Chandigarh University's University Institute of Computing, as well as a master's and bachelor's degree in Computer Science from the University of Kashmir. Dr. Bukhari's expertise lies in applying Artificial Intelligence, Machine Learning, and Deep Learning to tackle complex challenges across interdisciplinary domains. His research in Immunoinformatics and Computational Biology focuses on epitope-based vaccine design and identifying primary vaccine candidates, thereby accelerating the discovery of novel therapeutics. In Medical Image Analysis, his work leverages deep learning techniques to enhance diagnostic precision and clinical outcomes, driving innovation in healthcare technology. Dr. Bukhari has contributed to numerous peer-reviewed publications in high-impact journals, conference proceedings, and book chapters. He is also the author and editor of several books published by leading publishers such as Taylor and Francis-CRC Press. Dr. Bukhari actively collaborates with scientists from prominent institutions both in India and abroad. He serves as a reviewer for prestigious journals and has chaired sessions at various international, Scopus-indexed conferences. His professional affiliations include memberships in the Institution of Electronics and Telecommunication Engineers (IETE) and the International Association of Engineers (IAENG). Dr. Bukhari’s research interests span Artificial Intelligence, Machine Learning, Deep Learning, Bioinformatics, Immunoinformatics, and Computational Biology, underscoring his commitment to advancing technology for global impact.

Khalid Raza

Department of Computer Science, Jamia Millia Islamia, New Delhi, India

Khalid Raza, an Associate Professor, leads the Computational Intelligence and Bioinformatics Lab at the Department of Computer Science, Jamia Millia Islamia (Central University) in New Delhi, India. With over 14 years of teaching and research experience, Dr. Raza has made significant contributions to bioinformatics and computational intelligence by developing algorithms and methods for computational biology. He has authored or co-authored more than 140 peer-reviewed research articles in renowned international journals, conference proceedings, and book chapters. Additionally, he has authored and edited 15 books published by distinguished publishers such as Springer-Nature, Elsevier, and CRC. Dr. Raza’s international academic experience includes a tenure as a Visiting Professor (ICCR Chair) at the Faculty of Computer & Information Sciences, Ain Shams University in Cairo, Egypt, during the 2017–2018 academic session. He serves as an Academic Editor for PeerJ Computer Science and as a guest editor for Computational Frontiers in Medicinal Chemistry and Harnessing Nature’s Pharmacy for the Therapeutics of Lung and Respiratory Diseases in Mini-Reviews in Medicinal Chemistry and Natural Product Communication, respectively. He also previously Guest Edited a Special Collection on “Phytochemicals against SARS-CoV-2 Infection” for Natural Product Communications. Dr. Raza actively collaborates with esteemed scientists from leading institutions in India and around the world, including AIIMS (New Delhi), CSIR-IGIB, DRDO-INMAS, UFMG (Brazil), and UoT Sydney (Australia).

Collection Overview

Computational biology and bioinformatics are pivotal in advancing our understanding of biological systems and addressing complex challenges in healthcare and biotechnology. This Methods Collection focuses on a broad spectrum of research areas, including genomics, next-generation sequencing (NGS), systems biology, and computer-aided drug design (CADD). It also emphasizes the integration of artificial intelligence (AI), machine learning (ML), and deep learning (DL) approaches in drug discovery, multi-epitope vaccine design, and other emerging areas within biological domains. These advancements are transforming the way biological data is analyzed, interpreted, and applied to real-world problems.

The aim of this Methods Collection is to provide researchers with innovative computational methodologies and tools that are driving these advancements. It will cover diverse topics such as genome annotation, network modeling in systems biology, AI-driven drug repurposing, structural bioinformatics, and the use of ML/DL for predicting molecular interactions and vaccine efficacy. Additionally, contributions will explore data integration and visualization techniques, highlighting their role in overcoming key challenges in biological research.

By showcasing state-of-the-art methods and applications, this collection will serve as a valuable resource for the research community. It will facilitate knowledge sharing, encourage interdisciplinary collaboration, and support the development of new computational approaches to tackle pressing biological questions.