held in conjunction with IEEE BIBM 2026
Dallas, Texas
December 1-4, 2026
https://www3.cs.stonybrook.edu/~bibm2026/
CALL FOR PAPER
The goal of the workshop is to bring together researchers and practitioners working in bioinformatics, biomedicine, medical informatics, and computational biology, as well as scientists from related areas in biology and medicine, to discuss foundational methods of network analysis and their evolution through Artificial Intelligence (AI) techniques.
The workshop aims to collect original research papers, reviews, and case studies describing both classical and AI-enhanced network analysis methods, with particular emphasis on their applications in computational biology, bioinformatics, medicine, and public healthcare.
Special attention will be devoted to works that combine network-based modeling with representation learning, graph neural networks, and machine learning, while preserving interpretability and biological insight.
Contents:
The modeling and investigation of complex systems through graphs integrating biological, biomedical, and clinical data represent a central topic for the research community.
The study of associations at a system-level scale has shown remarkable potential, making network analysis a de-facto standard in computational biology, with applications spanning from molecular interaction networks to brain connectome analysis.
Network analysis methods provide a shared methodological framework to represent relationships among entities and to study the structures emerging from recurring interactions.
The underlying assumption is that a deeper understanding of biological and clinical phenomena can be achieved by analyzing relationships rather than isolated components.
Traditional network analysis approaches focus on:
· the characterization of a single network, extracting global and local topological properties;
· the comparison of multiple networks, to study conservation, divergence, and evolution of interactions across conditions, time, or species.
Examples include the identification of statistically overrepresented subgraphs to detect functional modules, the discovery of protein complexes through highly connected regions, and the system-level analysis of interactions among DNA, RNA, microRNA, proteins, and small molecules to identify functional pathways.
In recent years, Artificial Intelligence and Representation Learning have significantly extended classical network analysis.
In particular, Network Representation Learning (NRL), Cascade Representation Learning (CRL), and Graph Neural Networks (GNNs) enable learning expressive, low-dimensional representations of nodes, edges, and entire networks, supporting prediction, integration of heterogeneous data, and scalable analysis.
The workshop positions itself at the intersection between foundational network analysis and AI-driven learning on graphs, promoting approaches that combine theoretical rigor, computational efficiency, and biological interpretability.
Interest to the BIBM community
Biological network analysis plays a key role in many biomedical applications, including:
identification of drug targets and disease modules;
functional characterization of genes and proteins of unknown function;
design of effective strategies for infectious and complex diseases;
early diagnosis of neurological disorders through the detection of abnormal network patterns.
By integrating network science and artificial intelligence, the workshop aligns with the mission of the BIBM community to promote innovative, interdisciplinary, and data-driven approaches for biological and medical research.
The main motivation of the workshop is to collect advanced contributions on the development of new pipelines, algorithms, and tools for the analysis of complex systems across different domains, with a strong focus on network-based AI methods.
3. Research topics included in the workshop
The workshop seeks original research papers presenting foundational, methodological, and applied contributions to network analysis and artificial intelligence for complex systems.
Topics of interest include, but are not limited to:
· Network-based bioinformatics methods
· Network-based applications in computational biology, genomics, medicine, and healthcare
· Graph representation learning for biological and biomedical data
· Graph Neural Networks (GNNs) and deep learning on biological networks
· Network-based modeling and analysis of complex diseases
· Disease module detection and pathway-centric network analysis
· Network alignment and network comparison
· Network embedding and representation learning
· Pathway analysis and functional enrichment methods
· Interactomics and pathway databases
· Complex network models for structure and function analysis
· Network models in epidemiology
· Next-generation network science
· Artificial intelligence for network models of complex diseases
· AI-driven prediction and inference on biological networks
· Scalable and high-performance computing for network analysis
PROGRAM
The workshop will take place on December 1-4, 2026 (To Be Announced). The program is not available yet.
PAPER SUBMISSION, REGISTRATION AND PUBLICATION
Please submit a full-length paper (up to 8 page IEEE 2-column format) through the BIBM-2026 Workshops submission system:
https://wi-lab.com/cyberchair/2026/bibm26/index.php
You can download the format instruction here:
http://www.ieee.org/conferences_events/conferences/publishing/templates.html
Electronic submissions (in PDF or Postscript format) are required. Selected participants will be asked to submit their revised papers in a format to be specified at the time of acceptance.
IMPORTANT DATES
Oct 15, 2026 : Due date for full workshop papers submission
Oct 20, 2026 : Notification of paper acceptance to authors
Oct 25, 2026 : Camera-ready of accepted papers
Dec 1-4, 2026: Workshops
WORKSHOP ORGANIZER
Marianna Milano, University Magna Graecia of Catanzaro, Italy
m.milano@unicz.it
PROGRAM COMMITTEE (TO BE CONFIRMED)
Giuseppe Agapito, University Magna Graecia of Catanzaro, Italy
Loris Belcastro, University of Calabria, Italy
Marzia Settino, University Magna Graecia of Catanzaro, Italy
Anna Bernasconi, Politecnico di Milano, Italy
Paola Lecca, Free University of Bozen-Bolzano
Chiara Pastrello Krembil Research Institute (KRI), Toronto, Canada
Antonio Guerrieri, ICAR-CNR, Italy
Fabrizio Marozzo, University of Calabria, Italy
Pietro Cinaglia, University Magna Graecia of Catanzaro, Italy
Zeeshan Abbas, Jeonbuk National University, South Korea
Pietro Pinoli, Politecnico di Milano, Italy
Luigi Alfonso, University of Calabria , Italy