Call for Papers: Theme Issue: Machine Learning-Based Predictive Models Using Genomic Data

JMIR Publications is pleased to announce a new theme issue titled “Machine Learning-Based Predictive Models Using Genomic Data” in JMIR Bioinformatics and Biotechnology—a PubMed/SCOPUS-indexed, peer-reviewed journal focusing on bioinformatics, computational biology, and biotechnology. This theme issue aims to explore cutting-edge research at the intersection of machine learning and genomics, fostering advancements in predictive modeling for biological insights.

Scope and Topics

JMIR Bioinformatics and Biotechnology welcomes submissions from researchers, educators, and practitioners from around the world. Potential topics include but are not limited to: 

  • Development and application of machine learning algorithms for phenotype prediction using genomic data
  • Integration of multi-omics data for predictive modeling
  • Prediction of biological outcomes, disease associations, and functional genomics
  • Novel methodologies for handling large-scale genomic data sets

Please visit our website for more information on submission guidelines and the peer review process.

Submission Guidelines: 

All submissions will undergo rigorous peer review, and accepted articles will be published as part of a special issue titled “Machine Learning-Based Predictive Models Using Genomic Data.”

Submission Deadline: May 31, 2024

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Submissions not reviewed or accepted for publication in this JMIR Bioinformatics and Biotechnology theme issue may be offered cascading peer review or transfer to other JMIR Publications journals, according to standard publisher policies. For example, early-stage formative work that informs the design of future interventions or research may better fit the scope for JMIR Formative Research. Authors are encouraged to submit study protocols or grant proposals to JMIR Research Protocols before data acquisition to preregister their study (ie, as registered reports—subsequent acceptance in one of the JMIR Publications journals is then guaranteed).

Researchers without institutional support or grant funding can apply for a full article processing fee (APF) waiver using this application form.