Submit your abstract via this form by 23 March 2026.
Authors must select the most appropriate track and indicate whether they wish to be considered for a talk or poster.
Abstracts should clearly and concisely describe the background, methods, results, and significance of the work, making full use of the space provided.
All submissions are reviewed by the BITS2026 Program Committee and two to three anonymous referees.
Abstracts submitted for oral presentation that are not selected for a talk will automatically be considered for poster presentation.
Available tracks
- AI, Models and Tools
- Omics data, methods and applications
- Systems biology and multi-omics
- Proteins
- Databases, interoperability and research infrastructure
- Translational bioinformatics
Special tracks
Spatial omics
The transition from "smoothie" bulk sequencing to "map-based" spatial architecture represents the most significant paradigm shift in modern genomics. As we convene for BITS 2026, spatial omics has moved beyond mere visualization into a quantitative powerhouse, enabling us to decode the "sociology" of cells. This special track explores the computational challenges inherent in this 2026 landscape: the integration of sub-cellular resolution data, the development of foundation models for spatial deconvolution, and the mapping of the "interactome"—how cells communicate across tissue niches to drive pathology.
We will highlight advancements in Italian bioinformatics, focusing on algorithmic solutions for aligning multi-modal spatial datasets and the move toward 3D tissue reconstruction. In an era where location is as critical as identity, we examine how spatial transcriptomics and proteomics are redefining our understanding of the tumor microenvironment and neurodegenerative spatial patterns. This session serves as a nexus for researchers bridging the gap between high-throughput molecular biology and spatially-aware deep learning, aiming to transform raw spatial coordinates into actionable biological insights and clinical biomarkers.
Cancer bioinformatics
This special track explores the technical and methodological convergence of medical imaging and molecular bioinformatics.The analysis of cancer has shifted toward a multi-modal approach where digital pathology and radiological data are integrated with genomic profiles to better understand tumor heterogeneity. For researchers primarily focused on imaging, this session examines how quantitative image features—ranging from sub-visual textures to spatial cellular distributions—can serve as biomarkers for specific genetic alterations and clinical outcomes.
We will discuss the computational challenges of this integration, including the development of multi-modal deep learning architectures, such as vision transformers and graph neural networks, designed to handle disparate data scales. The program highlights Italian contributions to the field, focusing on standardized pipelines for feature extraction, data fusion techniques, and the interpretability of "black-box" models in a clinical setting. This session provides a forum for discussing how the synergy between morphological and molecular data can improve diagnostic accuracy and the prediction of therapeutic resistance.
