Special Issue on Biomedical Data Science

Aims and Scope

Statistical Learning and Data Science (SLADS) is a newly launched journal sponsored by the Chinese Academy of Sciences, dedicated to publishing high-quality research across statistics, machine learning, artificial intelligence, and data science. SLADS emphasizes both rapid publication and rigorous peer review, using the OpenReview system to ensure transparency and quality. The editorial goal is to reach an Accept/Reject decision within 3.5 months of submission while maintaining high scholarly standards.

SLADS is pleased to announce a Special Issue on Biomedical Data Science, showcasing innovative statistical learning and data science methodologies that advance biomedical discovery and improve clinical and public health decision-making. This Special Issue invites novel methodologies and impactful applications that develop, evaluate, or translate statistical and machine learning techniques for biomedical data. We are especially interested in submissions that connect rigorous methodology with real-world biomedical problems.

Submission Deadline: June 30, 2026

• Early submissions will be reviewed and published online ahead of the final issue.

Topics of Interest

Submissions may address (but are not limited to) the following topics:

• Electronic Health Records and Real-World Data
• Imaging
• -Omics
• Wearables, Mobile Health, and Digital Biomarkers
• Clinical Trials, Adaptive Designs, and Evidence Synthesis
• Causal Inference and Decision-Making in Biomedicine

We welcome submissions that provide novel methodology with theoretical justification and empirical evaluation, high-impact biomedical applications, and transparent and reproducible research, including well-documented code and evaluation details where feasible.

We also welcome reviews/mini-reviews, perspectives, benchmarking studies, and tutorials on significant topics in biomedical data science. For these types of submissions, please send a proposal of about 300 words summarizing the main sections and key references to the Managing Editor, Ruiyan Zhang (zhangry@scichina.com), who will forward it to the guest editors for review prior to formal submission.

For all submissions, please make the title and abstract understandable to a broader readership, not only statisticians. The datasets, scientific questions, and practical importance should be clearly introduced, together with detailed illustrations and discussions of the results and their implications.

Guest Editors

• Rui Duan, Harvard University
• Can Yang, The Hong Kong University of Science and Technology
• Hongyu Zhao, Yale University
• Tao Wang, Shanghai Jiao Tong University
• Bingxin Zhao, University of Pennsylvania

Submission Information

Manuscripts should be submitted through the SLADS website at http://slads.scichina.com.

Contact: Ruiyan Zhang, zhangry@scichina.com