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BMC Bioinformatics

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インパクトファクター:
3.3
出版社:
BioMed Central
ISSN:
1471-2105
閲覧:
31631
フォロー:
24

論文募集

BMC Bioinformatics is an academic journal published by BioMed Central. (ISSN 1471-2105, impact factor 3.3, CCF C).

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.
最終更新：Dou Sun（）

Special Issues

Special Issue on Predictive toxicology 投稿締切日: 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.
最終更新：Dou Sun（）

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