The goal of the BIOKDD 2026 is to encourage KDD researchers to solve the numerous problems and challenges in Bioinformatics using Data Mining technologies. Based on the organizers’ expertise and communities for this year’s BIOKDD workshop, we will feature the theme “Digital Twins”, which enables the simulation of disease trajectories, microenvironmental interactions, cell-cell communication, and post-treatment outcome prediction. We also welcome broader research applying data CFP: 25th International Workshop on Data Mining in Bioinformatics (with SIGKDD'26)
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BIOKDD 2026
(Held in Conjunction with SIGKDD 2026)

August 9-13, 2026
Jeju, Korea
http://biokdd.org/biokdd26/


INTRODUCTION
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The goal of the BIOKDD 2026 is to encourage KDD researchers to solve the numerous problems and challenges in Bioinformatics using Data Mining technologies. Based on the organizers’ expertise and communities for this year’s BIOKDD workshop, we will feature the theme “Digital Twins”, which enables the simulation of disease trajectories, microenvironmental interactions, cell-cell communication, and post-treatment outcome prediction. We also welcome broader research applying data mining to address biomedical problems. The key goal is to accelerate the convergence between Data Mining and Bioinformatics communities to expedite discoveries in basic biology, medicine and healthcare.

PUBLICATION OPTION
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As a tradition of BIOKDD, accepted original submissions will be invited to publish in BIOKDD Special Issue on IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB).

TOPICS OF INTEREST
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• 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

IMPORTANT DATES
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Submission Due: April 30, 2026
Notification Date: June 4, 2026
Workshop Date: August 9-13, 2026


SUBMISSION GUIDELINES
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We solicit submission of papers in the following three categories:

• Regular Track: original papers without duplicate submission. Papers should be at most 10 pages in length, and using ACM Proceedings Format (https://www.acm.org/publications/proceedings-template) is recommended.

• Abstract Track: 1-page limit, for introducing preliminary research outcomes, with emphasis on research effectiveness on real-world datasets.

• Late-Breaking Research: submission of a published manuscript (at least in arXiv) along with additional supplemental/unpublished data, to highlight the work's impact.

All papers will be peer-reviewed. If accepted, at least one author should attend the workshop to present their work. The papers should be in PDF format and submitted via Microsoft CMT: https://cmt3.research.microsoft.com/BIOKDD2026


ORGANIZERS
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Program Co-Chairs:
• Zongliang Yue (Auburn University)
• Truong-Son Hy (The University of Alabama at Birmingham)

General Chairs:
• Da Yan (Indiana University Bloomington)
• Mohammed J. Zaki (Rensselaer Polytechnic Institute)
• Jake Chen (The University of Alabama at Birmingham)

CONTACT
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Please email all questions about submissions to zzy0065@auburn.edumining to address biomedical problems. The key goal is to accelerate the convergence between Data Mining and Bioinformatics communities to expedite discoveries in basic biology, medicine and healthcare.

As a tradition of BIOKDD, accepted original submissions will be invited to publish in BIOKDD Special Issue on IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB).

TOPICS OF INTEREST
• 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