AI Biology & Medicine (AIBM) publishes high-quality original research and reviews across a broad range of topics in artificial intelligence, computational biology, and biomedical data science. AI holds transformative potential to accelerate scientific discovery, decode complex biological systems, and reveal emergent properties of molecular networks relevant to human health and disease, yet its integration into biology and medical research raises important questions around data integrity, algorithmic reproducibility, model interpretability, and the responsible application of computational approaches to biological discovery. AI Biology & Medicine provides a platform to discuss these opportunities and challenges—encouraging cross-disciplinary dialogue among AI researchers, biologists, computational scientists, and biomedical researchers—through original research, review, conceptual analysis, methods & protocols, software & database, brief communication, perspective, highlight, and correspondence.

The journal welcomes contributions across, but not limited to, the following five interconnected domains:

1. Computational Biology and Bioinformatics

Development and application of AI-driven methods for analyzing biological sequences, structures, and systems. Includes supervised, unsupervised, and deep learning approaches applied to genomic, proteomic, metabolomic, and imaging data for biological discovery.

2. AI-Powered Platforms, Models, and Methodological Tools

New computational frameworks, platforms, models, automation, biosensors, and imaging systems enabling systems-level understanding in biology and medicine. Encompasses foundational and applied methodologies that advance AI applications, including automated reasoning systems for hypothesis generation, experimental design, and knowledge discovery.

3. Intelligent Data Mining and Data Integration

Novel AI approaches for extracting knowledge from complex, high-dimensional biological and biomedical datasets. Encompasses advanced methods for harnessing diverse data sources—including omics, imaging, clinical records, and literature—to enable deeper insights and integrative analyses across biological and biomedical domains.

4. Cross-Disciplinary AI Integration in Biology and Medicine

Studies that bridge AI with experimental research, fostering synergy between computational modeling and laboratory investigation. Emphasizes the translation of AI-driven predictions into testable biological hypotheses and mechanistic understanding.

5. Adaptation of AI Concepts for Biomedical Applications

Translation and tailoring of core AI concepts—such as reasoning, planning, representation learning, and knowledge representation—from computer science to address challenges in biological research and biomedical knowledge discovery.

Editorial Board