Aims & Scope

Aims:

LifeAI is a gold open-access journal and aims to foster the translation of computational breakthroughs into real-world biological insights and applications, supporting both academic researchers and industry practitioners. The journal emphasizes reproducible high-quality research with practical relevance. It is published quarterly online by Scilight Press.

Submissions to LifeAI may include original research articles, reviews, case studies, software tools, and application notes, contributing to the ongoing development and practical impact of AI in life sciences.

Scope:

LifeAI is a premier, peer-reviewed journal dedicated to advancing research at the intersection of artificial intelligence (AI) and molecular life sciences. The journal provides a multidisciplinary platform for disseminating innovative methods, tools, and applications of AI in bioinformatics, computational biology, genomics, proteomics, systems biology, and related fields.

The journal welcomes contributions that demonstrate the practical application of AI and machine learning in addressing complex biological challenges, including but not limited to:

  • Development and deployment of machine learning algorithms for biological data analysis.
  • Development of explainable AI methods to enhance interpretability and reliability in life sciences.
  • AI applications in molecular modeling, simulation, and prediction of biomolecular interactions.
  • AI for structural biology, including protein folding and design.
  • AI-driven insights into genomics, transcriptomics, proteomics, epigenomics, metabolomics, and other OMICs data.
  • AI models and simulations for understanding molecular networks and cellular processes.
  • Development of tools for multi-omics integration and interpretation.
  • AI in precision medicine, diagnostics, and understanding disease mechanisms at the molecular level.
  • AI applications in evolutionary biology, phylogenetics, and biodiversity studies.
  • Intelligent systems for biological image analysis and medical diagnostics, as long as they are combined with molecular data.
  • Natural language processing (NLP) for biomedical text mining and literature analysis.
  • AI-based approaches for personalized medicine and precision biology.
  • Ethical considerations, model interpretability, and reproducibility in AI-driven biological research.
  • Impact of AI on data privacy, security, and equitable access to technologies.

Topics that do not directly deal with molecular data are not within the scope of this journal. For example, submissions focused exclusively on pure image analysis, such as medical imaging, pathology image classification, or general computer vision techniques without a molecular context, fall outside the journal's remit. Further examples of the topics that are not within the scope of the journal include purely mathematical papers, even if they use biological data as test examples, studies in theoretical biology, mathematical models and simulations of biological systems, and epidemiological studies.