Computational Science is a rapidly growing multi- and interdisciplinary field. It develops mathematical and computational models and uses advanced computing techniques to simulate these models, driven by data. Its overarching goal is to understand and solve complex problems. It has reached a level of predictive and interventional capability that now firmly complements the traditional pillars of experimentation and theory.

The recent advances in experimental techniques have opened up new windows into physical and biological processes at many levels of detail. The resulting data explosion allows for detailed data-driven modeling and simulation which is no longer feasible using traditional analytical approaches alone.

This new discipline in science combines computational thinking, modern computational methods, devices and collateral technologies to address problems far beyond the scope of traditional numerical methods.

Computational science typically unifies three distinct elements:

• Modeling, Algorithms and Simulations (e.g. numerical and non-numerical, discrete and continuous);
• Software developed to solve science (e.g., biological, physical, and social), engineering, medicine, and humanities problems;
• Computer and information science that develops and optimizes the advanced system hardware, software, networking, and data management components (e.g. problem solving environments).

The Journal of Computational Science aims to be an international platform to exchange novel research results in simulation-based science across all scientific disciplines. It publishes advanced innovative, interdisciplinary research where complex multi-scale, multi-domain problems in science and engineering are solved, integrating sophisticated numerical methods, computation, data, networks, and novel devices.

The journal welcomes original, unpublished high quality contributions in the field of computational science at large, addressing one or more of the aforementioned elements.

Special Issue on Computational Challenges in Bioinformatics and Networks Science: Artificial Intelligence, Bioinformatics and Network Science for the Integrated Analysis of Molecular and Clinical Data
Submission Date: 2026-01-20

Biomedical and bioinformatics research face constant challenges that require innovative computational science solutions. High-throughput technologies, clinical diagnostics, and multi-scale biological data produce enormous volumes of heterogeneous information that must be efficiently acquired, stored, processed, analyzed, and visualized.

Among the others, Bioinformatics and Network Science methods are used to integrate and analyze these data with the help of Artificial Intelligence, to improve knowledge extraction, and High Performance Computing to reduce execution time.

This Special Issue aims to face the research problems discussed in two workshops held in the program of the Workshops on Computational Science (WCS), a set of thematic workshops held in conjunction with the International Conference on Computational Science (ICCS), namely the Biomedical and Bioinformatics Challenges for Computer Science (BBC) and the Artificial Intelligence Approaches for Network Analysis (AIxNA) workshops.

This Special Issue invites contributions addressing new computational methods, high-performance architectures, distributed computing, mathematical modeling, and advanced data pipelines to support the modeling and simulation of biological systems, organs, and organisms.Moreover, we encourage submissions exploring machine learning, graph neural networks, geometric deep learning, probabilistic models, and scalable AI algorithms applied to gene regulatory networks, protein-protein interactions, disease-gene associations, epidemic modeling, and drug repurposing.

Topics include high-performance pipelines for biomedical data analysis, high-performance architectures for the modeling and simulation of biological systems, organs, and organisms, high-performance and distributed AI systems for large-scale and noisy network data, algorithms for community detection, link prediction, network dynamics, and the use of network geometry or hyperbolic embeddings for biological inference.

By connecting BBC and AIxNA, this Special Issue aims to create an interdisciplinary platform for sharing novel computational strategies that drive discoveries in biology, medicine, and public health through the synergy of computer science, bioinformatics, and AI-driven network analysis.

Guest editors:

Prof. Mario Cannataro (Executive Guest Editor)
University Magna Graecia of Catanzaro, Catanzaro, ITALY 
Email: cannataro@unicz.it
Areas of Expertise: Bioinformatics, Data Mining; Parallel Computing; Artificial Intelligence; Computational Science

Assoc. Prof. Marianna Milano
University Magna Graecia of Catanzaro, Catanzaro, ITALY
Email: m.milano@unicz.it
Areas of Expertise: Bioinformatics; Network Analysis

Manuscript submission information:

Important Dates:

    Manuscript Submission Deadline: January 20, 2026
    Editorial Acceptance Deadline: May 20, 2026

Manuscripts must be submitted via the Journal of Computational Science online submission system (Editorial Manager®). Please select the article type “VSI: AI-Bioinformatics-Networks” when submitting your manuscript online.

Please refer to the Guide for Authors to prepare your manuscript. 

For any further information, the authors may contact the Guest Editors.

Keywords:

• Artificial Intelligence in Bioinformatics and Network Science
• Machine learning (supervised, unsupervised, deep learning) for analysis of clinical data
• Graph neural networks (GNNs, geometric deep learning)
• Biomedical data (large-scale, multi-omics, clinical datasets)
• Network-based analysis (gene regulatory, protein-protein interactions)
• Disease-gene association studies (network methods)
• High-performance computing (HPC architectures, GPU, parallel processing)
• Distributed computing (grid, cloud, peer-to-peer infrastructures)
• Computational modeling and simulation (biological systems, epidemic spreading)
• Network geometry (hyperbolic embeddings, topological methods)