JBD publishes original research, reviews, methods papers, and data resources that apply bioinformatics to diabetes and metabolic disease. Submissions should demonstrate rigorous computational methods and clear clinical or biological interpretation.

• Title page with author affiliations and corresponding author details
• Structured abstract with objectives, methods, results, and conclusions
• Introduction that defines the diabetes context and research gap
• Methods with detailed data sources, preprocessing, and analytics
• Results with validation metrics and clear interpretation
• Discussion linking findings to diabetes outcomes or mechanisms
• Conclusion highlighting key contributions and future work

• Use clear headings and consistent terminology
• Define abbreviations and gene or protein symbols at first use
• Provide units for all measurements and clinical metrics
• Include figure legends that describe sample sizes and data sources
• Prepare tables in editable format with clear headings
• Label supplementary files clearly and reference them in the text

Authors should provide data availability statements and, when possible, deposit data in trusted repositories. Code or pipelines should be shared or documented to support reproducibility.

• Include accession numbers or repository links
• Describe preprocessing and quality control steps
• Document software versions and computational environments
• State any access restrictions for sensitive data

• Submit high resolution figures in standard formats
• Label axes and include units for quantitative plots
• Provide color blind friendly palettes when possible
• Ensure tables are editable and include footnotes

• Ensure references are complete and consistent
• Include DOIs where available
• Use standard citation formats
• Confirm all in text citations appear in the reference list

• Provide ethics approval and consent statements
• Disclose conflicts of interest and funding sources
• Follow reporting standards for clinical or omics studies
• Describe limitations and generalizability

JBD uses single blind peer review. Reviewers evaluate study rigor, data quality, and clarity of reporting. Initial decisions are typically issued within two to four weeks depending on reviewer availability.

Accepted manuscripts move to production for copyediting, proof review, and DOI registration. Articles are published under CC BY 4.0 to support open access reuse with attribution.

Authors should review proofs promptly to confirm accuracy of figures, tables, and metadata.

APCs are applied after acceptance and cover peer review management, production, and archiving services. Partial waivers may be available for eligible authors. Contact the editorial office for guidance.

• Scope alignment confirmed
• Data availability statement included
• Validation results reported
• Cover letter prepared with scope summary
• Ethics and consent statements included

For questions about formatting or submission steps, contact [email protected].

• Describe cohort selection, inclusion criteria, and diabetes phenotype definitions so readers understand the population context.
• Provide metadata about biospecimens, including collection timing relative to treatment, fasting status, and storage conditions.
• Report preprocessing steps for sequencing or omics data, including alignment tools, reference genomes, and quality thresholds.
• Include normalization approaches for multi-omics integration, such as batch correction or scaling methods.
• Clarify how missing data were handled, especially in longitudinal or wearable datasets.
• Provide version numbers for software, libraries, and pipelines to support reproducibility.
• Report performance metrics for predictive models, including calibration, discrimination, and external validation results.
• Explain feature selection strategies and how overfitting risks were mitigated.
• Describe cross validation or train test splits and confirm that data leakage was avoided.
• Share accession numbers or repository links for public datasets and code when possible.
• Provide a concise data availability statement that notes any access restrictions for sensitive clinical data.
• Report ethical approvals and consent procedures for human datasets or biobanks.
• Include clinical context for diabetes subtypes, such as type 1, type 2, gestational, or secondary diabetes.
• Define glycemic metrics clearly, including HbA1c, fasting glucose, or continuous glucose monitoring summaries.
• Describe how comorbidities or medications were accounted for in the analysis.
• If using wearable or digital health data, report device validation and sampling frequency.
• Provide details on integration of electronic health records, including coding systems and quality checks.
• Discuss limitations related to population bias, data sparsity, or missing clinical covariates.
• Explain interpretability methods used for machine learning models and their clinical relevance.
• Report sensitivity analyses that test robustness across cohorts or subgroups.
• Describe how class imbalance was addressed in predictive models for complications.
• Clarify computational resource requirements and runtime considerations for large scale analyses.
• Provide standard evaluation metrics for clustering or stratification analyses.
• If biomarkers are proposed, describe validation evidence and biological plausibility.
• Explain how multi-site data were harmonized and how site effects were controlled.
• Include quality control plots or summary statistics for omics data.
• Describe how data sharing aligns with FAIR principles when possible.
• Provide documentation for custom scripts or pipelines that are not publicly available.
• Highlight reproducibility steps such as containerization or workflow managers.
• Report how model thresholds were selected for risk prediction tools.
• If synthetic data are used, describe generation methods and validation checks.
• Discuss how genomic variants were annotated and interpreted in a diabetes context.
• Provide evidence for clinical utility, such as decision impact or workflow integration.
• Explain how uncertainty was quantified for model outputs and predictions.
• Describe how longitudinal trajectories were modeled for glycemic control or complications.
• Include a short statement on limitations and future validation needs.
• Provide clear definitions for endpoints like incident diabetes or remission.
• If imaging data are used, report acquisition parameters and analysis software.
• Clarify how multi-ethnic representation was handled and reported.
• Provide transparency on funding sources and potential conflicts of interest.
• Provide a structured abstract with clear objectives, methods, results, and conclusions.
• Use consistent gene or protein nomenclature and define abbreviations at first use.
• Include a data availability statement with repository links or accession numbers.
• Provide details on software versions, packages, and computational environments.
• Report statistical tests, effect sizes, and confidence intervals where applicable.
• Describe cohort demographics, inclusion criteria, and diabetes phenotype definitions.
• Include descriptions of normalization, batch correction, or imputation procedures.
• Provide clear figure legends and indicate sample sizes and data sources.
• Confirm references include DOIs where available and match in text citations.
• Disclose funding sources, grant numbers, and potential conflicts of interest.
• Report ethical approvals and consent for human or animal datasets.
• Clarify how model thresholds or cutoffs were chosen for prediction tasks.
• Include a brief limitations statement that addresses generalizability.
• If code is shared, provide repository links and licensing details.
• State whether preprints exist and disclose prior dissemination.
• Describe how raw data and code can be accessed, including access controls.
• Provide sample identifiers, study timelines, and follow up intervals.
• Confirm all tables are editable and include clear headings and units.
• Report clinical trial registration identifiers when applicable.
• Include data governance statements for sensitive clinical or patient data.
• Describe biospecimen handling, storage, and chain of custody procedures.
• Provide statements on AI assisted analysis when used and specify validation steps.

JBD is committed to rigorous, transparent publishing in bioinformatics and diabetes research. We emphasize reproducible computational methods, clear data provenance, and ethical compliance across all article types.

The editorial office supports authors, editors, and reviewers with clear guidance and responsive communication. For questions about scope or workflow, contact [email protected].

We encourage continuous improvement in reporting practices and share updates that help the community maintain high standards in computational and translational diabetes research.