Research Topic

The Potential of Machine-learning in Pharmacogenetics, Pharmacogenomics and Pharmacoepidemiology

About this Research Topic

Recent advances in (deep) machine learning, natural language processing, and information retrieval show great potential for enhancing the knowledge and processes in the fields of pharmacogenetics, pharmacogenomics, and pharmacoepidemiology. These techniques allow for the unprecedented analysis of large (unstructured) datasets that would otherwise be intractable, such as finding relevant patterns and clusters in the data being analyzed. From predicting treatment outcomes and guiding drug choice to prevent adverse reactions to drug discovery and repurposing, artificial intelligence holds great promise when it comes to the development of support systems to aid clinical decision making in the coming years.

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.

Recent advances in (deep) machine learning, natural language processing, and information retrieval show great potential for enhancing the knowledge and processes in the fields of pharmacogenetics, pharmacogenomics, and pharmacoepidemiology. These techniques allow for the unprecedented analysis of large (unstructured) datasets that would otherwise be intractable, such as finding relevant patterns and clusters in the data being analyzed. From predicting treatment outcomes and guiding drug choice to prevent adverse reactions to drug discovery and repurposing, artificial intelligence holds great promise when it comes to the development of support systems to aid clinical decision making in the coming years.

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.

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Submission Deadlines

31 August 2021 Abstract
31 December 2021 Manuscript

Participating Journals

Manuscripts can be submitted to this Research Topic via the following journals:

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Topic Editors

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Submission Deadlines

31 August 2021 Abstract
31 December 2021 Manuscript

Participating Journals

Manuscripts can be submitted to this Research Topic via the following journals:

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