Dear colleagues,
Statistical Applications in Genetics and Molecular Biology (SAGMB) has announced a special issue on Statistical AI Methods for Molecular Biology and Omics, and we would love to see submissions from this community.
The issue aims to highlight novel statistical methods, applications, and reviews that advance the principled use of AI in omics. Topics include, but are not limited to: transformer-based and foundation models for molecular and omics data; agentic and adaptive AI for hypothesis generation and experimental design; statistical transfer learning and domain adaptation; uncertainty quantification, robustness, and calibration; hybrid statistical-AI and Bayesian frameworks; multimodal, multiview, and multistudy integration; spatial and single-cell omics; biologically grounded causal inference; and review articles, tutorials, and best-practice papers.
Submissions are open now through December 31, 2026, at https://mc.manuscriptcentral.com/dgsagmb. Please note in your cover letter that the manuscript is for the "Statistical AI Methods for Molecular Biology and Omics" special issue. All papers go through the journal's standard peer review. There are no publication fees unless you opt for open access, in which case a 30% discount applies.
The full call is on the journal website: https://www.degruyterbrill.com/journal/key/sagmb/html#specialIssues
Questions are welcome at himel.mallick@cornell.edu or acharyya@uab.edu. Please feel free to forward this to students, postdocs, and collaborators who may be interested.
Best regards,
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Himel Mallick, PhD, FASA
Principal Investigator (Tenure-track Faculty)
Cornell University
New York, New York 10065
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