Machine Learning–Based Perspectives in Plant Biology
A special issue of International Journal of Molecular Sciences (ISSN 1422-0067). This special issue belongs to the section "Molecular Informatics".
Deadline for manuscript submissions: 31 May 2026
Special Issue Editor
Special Issue Information
Dear Colleagues,
Advances in high-throughput technologies have generated unprecedented volumes of molecular, phenotypic, and environmental data in plant science. Extracting biologically meaningful information from these complex datasets has become a major challenge. In this context, machine learning (ML) and artificial intelligence (AI) are emerging as powerful tools to identify relevant features, uncover hidden patterns, and support data-driven decision making in plant biology and biotechnology. These approaches offer new opportunities to prioritize key molecular drivers and accelerate the development of resilient and productive crops.
This Special Issue of the International Journal of Molecular Sciences aims to showcase recent advances in the application of machine learning and artificial intelligence to molecular and cellular plant research. The focus is on studies that integrate ML with genomics, transcriptomics, proteomics, metabolomics, phenotyping, and structural biology to enable feature selection, identification of key biological determinants, and prioritization of effective targets for plant breeding and biotechnology. Contributions addressing plant responses to biotic and abiotic stresses, plant–microbe or plant–virus interactions, and data-driven strategies for crop improvement are particularly encouraged. The topic is fully aligned with the scope of IJMS, emphasizing molecular mechanisms, computational biology, and innovative analytical approaches.
In this Special Issue, original research articles and review papers are welcome. Research areas may include, but are not limited to, the following:
- Machine learning methods for feature selection and gene prioritization;
- AI-driven integration of multi-omics datasets in plant biology;
- ML-based modeling of plant responses to biotic and abiotic stresses;
- Image-based phenotyping and predictive modeling;
- Computational approaches to plant–pathogen and plant–virus interactions;
- Data-driven strategies for molecular breeding and crop improvement;
- Explainable AI and model interpretation in plant science.
We look forward to receiving your contributions to this Special Issue.
Prof. Dr. Silas Pessini Rodrigues
Guest Editor
Manuscript Submission Information
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Keywords
- machine learning
- artificial intelligence
- plant molecular biology
- multi-omics integration
- feature selection
- gene prioritization
- plant stress responses
- high-throughput phenotyping
- crop improvement
- biotechnology
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