Aims and scope

BMC Bioinformatics is an open access, peer-reviewed journal that considers articles describing novel computational algorithms and software, models and tools, including statistical methods, machine learning and artificial intelligence, for the modelling and analysis of all kinds of biological data, as well as systems biology.

BMC Bioinformatics welcomes manuscripts in the following broad areas of research:

    Analysis and modelling of complex systems
    Comparative genomics 
    Data visualization
    Imaging, and image analysis
    Knowledge-based analysis
    Machine learning and artificial intelligence in bioinformatics
    Network analysis
    Proteomics
    Sequence analysis
    Structural analysis
    Transcriptome analysis

As a BMC Series journal, BMC Bioinformatics does not make editorial decisions based on the perceived interest or potential impact of a study. Manuscripts are considered for publication if they are scientifically valid. For research articles, this includes having a clearly defined and sound research question, appropriate methodology and analysis, and adherence to community-agreed standards relevant to the field.

Special Issue on Predictive toxicology
Día de Entrega: 2026-08-14

BMC Bioinformatics is welcoming submissions to our Collection on Predictive toxicology.

Predictive toxicology investigates the harmful effects of chemical substances using models and data-driven methods, often aiming to decrease dependence on traditional animal testing, such as mammals, for assessing health risks. Developments in this field support New Approach Methodologies (NAMs) for evaluating chemical safety and regulation. NAMs refer to any methods that enhance safety assessments while avoiding animal testing. Specifically, predictive toxicology employs computational techniques with a mechanistic understanding of toxicity to estimate risks to human health and the environment.

Recent advances have highlighted the use of various technologies that generate data valuable for in silico toxicity prediction, including omics, in vitro screening, high-throughput phenotyping, organoids, and alternative in vivo models. These innovations, combined with comparative biology and insights from other disciplines (e.g., genetics, evolution), refine hazard and risk assessment methods, facilitating a more precise evaluation of chemical safety and ultimately improving health outcomes.

This Collection welcomes submissions on the development of new computational and/or statistical approaches for predictive toxicology.

All manuscripts submitted to this journal, including those submitted to collections and special issues, are assessed in line with our editorial policies and the journal’s peer-review process. Reviewers and editors are required to declare competing interests and can be excluded from the peer review process if a competing interest exists.