Artificial Intelligence for Microbial Systems and Biomedical Applications

Advances in artificial intelligence (AI) are transforming the way we analyze, model, and interpret both microbial systems and complex biomedical data. AI-driven algorithms can automatically and objectively extract high-dimensional patterns from diverse data sources, including microbial imaging, multi-omics profiles, biomedical images, biosignals, and clinical data, thereby overcoming the subjective biases and scalability limitations of conventional analytical approaches. By bridging microbiology, bioinformatics, and biomedicine, AI is creating new opportunities not only for understanding microbial communities, functions, and host–microbe interactions, but also for improving disease characterization, diagnosis, prognosis, and translational research.This Research Topic aims to showcase cutting-edge AI methodologies and applications that advance microbial systems research while also enabling broader biomedical applications. We welcome original research articles and in-depth reviews that (1) introduce innovative computational methods, (2) demonstrate rigorous validation on microbiological, biological, biomedical, or clinical datasets, and/or (3) provide transferable workflows, benchmark resources, or software tools for the wider community. We particularly encourage interdisciplinary submissions that bring together expertise from computational science, microbiology, bioinformatics, biomedicine, and clinical research. Submissions describing new tools or algorithms should ideally include benchmarking against relevant standards and, where possible, provide publicly accessible code, datasets, or reproducible workflows.We especially encourage submissions in, but not limited to, the following areas:1. AI for Microbial and Biomedical Image Analysis:–Image classification, segmentation, detection, tracking, and quantitative analysis of microorganisms, microbial colonies, and microbial communities–Intelligent analytical methods for histopathology, cytology, medical microscopy, and other biomedical imaging modalities2. AI for Microbial and Biomedical Informatics:–Machine-learning methods for microbial genomics, metagenomics, transcriptomics, proteomics, and metabolomics–Machine-learning and data-mining approaches for multi-omics, clinical data, and molecular features3. AI for Microbial and Biomedical Signal Analysis:–Intelligent signal analysis methods for microbial growth dynamics, metabolic activities, experimental monitoring, and related temporal processes–Methods for the recognition, modeling, and anomaly detection of electrocardiography, electroencephalography, electromyography, and other biomedical signals4. AI for Microbial and Biomedical Multimodal Analysis:–Joint modeling of microbial images, omics data, biomedical images, biosignals, and clinical data–Cross-modal representation learning, data fusion, association analysis, and knowledge discovery for microbial systems research and biomedical applicationsPlease note that Systems Microbiology does not consider descriptive studies that are solely based on amplicon (e.g., 16S rRNA) profiles, unless they are accompanied by a clear hypothesis and experimentation, and provide insight into the microbiological system or process being studied. Topic editors: Jinghua Zhang, Mamunur Rahaman, Fan Liu, Jia Guo Source: https://www.frontiersin.org/research-topics/80480/artificial-intelligence-for-microbial-systems-and-biomedical-applications