About this Research Topic
This Research Topic invites new contributions in the field of artificial intelligence (machine learning, natural language processing, and information retrieval) that can be applied to advance pharmacogenetics, pharmacogenomics, and pharmacoepidemiology. The main goal is to gather novel methodologies, and examples of their translational use in clinical practice, as well as to identify the strengths and weaknesses of artificial intelligence in these fields.
We welcome submissions of Original Research, Systematic Reviews, Methods, Clinical Trials, Case Reports, Data Reports and Brief Research Reports addressing the development, improvement, or examples of using methods or algorithms including but not limited to:
• Data integration from diverse sources, and improvement of the inference of haplotypes and phenotypes, with a special focus on those inducing pharmacokinetic changes, generation of reactive metabolites, or those inducing adverse drug events.
• Prediction of drug-drug interactions and/or quantification of their dose-effect relationship.
• Acquisition of relevant pharmacogenetics and/or pharmacoepidemiology information from published sources or from clinical records by means of natural language processing.
• Managing large datasets useful in pharmacogenetics, such as DNA sequences obtained by next-generation sequencing, phenomics, transcriptomics, proteomics, or metabolomics.
• Improvement of risk/benefit assessment of drug use.
• Assessment of the probability of adverse drug effects in specific populations.
• Assessment of trends in drug use in specific populations.
Keywords: Machine learning, artificial intelligence, pharmacogenetics, pharmacogenomics, pharmacoepidemiology
Important Note: All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.