The Abstracts track is a perfect place for bioinformatics researchers and medical informaticians to introduce their preliminary research outcomes to increase research impact and to nurture collaborations. We emphasize research effectiveness on real-world datasets. Submitted abstracts will be peer-reviewed by the program committee to make sure that the content would be of interest to the KDD audience including researchers, practitioners and trainees.
Authors of accepted abstracts will be asked to give a short oral presentation. Quality ones will be invited for journal paper extension to publish in the BIOKDD Special Issue with IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB). We invite the submission of abstracts about research contributions in (but not limited to) the areas listed below:
- Digital twin frameworks for disease modeling, simulation, and therapeutic optimization
- Foundation models and representation learning for biological data
- Generative AI and large language models in bioinformatics
- Graph learning, knowledge graphs, and biological network modeling
- Single-cell and spatial omics modeling
- Multi-omics, multi-modal, and multi-scale data integration
- Genomics, variant analysis, and genotype–phenotype modeling
- Microbiome and ecological systems modeling
- Computational drug discovery, treatment simulation, and molecular design
- Biomarker discovery and precision medicine
- Biomedical imaging, clinical, and real-world health data analytics
- Uncertainty-aware, interpretable, and explainable AI for health
- Data management, benchmarking, reproducibility, and ethical deployment in biomedical AI
Submission Guidelines
Abstracts should be at most 1 page in length, single-spaced, in font size 10 or larger with one-inch margins on all sides. Using the ACM Proceedings Format is highly recommended.
An abstract should be submitted in PDF format through Microsoft CMT at the following link: https://cmt3.research.microsoft.com/BIOKDD2026. The submissions will be judged for quality and relevance through single-blind reviewing.
Acknowledgment
The Microsoft CMT service was used for managing the peer-reviewing process for this conference. This service was provided for free by Microsoft and they bore all expenses, including costs for Azure cloud services as well as for software development and support.