Bioinformatics and Diabetes invites high quality submissions that apply bioinformatics and data science to diabetes and metabolic research. We welcome original studies, reviews, and methodological advances that improve discovery, prediction, or clinical translation.

Our editorial focus prioritizes reproducible analytics, transparent reporting, and meaningful biological or clinical interpretation for diabetes prevention, diagnosis, and management.

• Genomic and polygenic risk modeling for diabetes
• Transcriptomics, proteomics, and metabolomics in metabolic disease
• Multi-omics integration and systems biology
• Machine learning for risk stratification and complications
• Clinical decision support and EHR driven insights
• Digital health, wearables, and continuous glucose monitoring analytics
• Biomarker discovery and validation for diabetes progression
• Population health, cohort studies, and real world data
• Drug response prediction and precision medicine
• Data harmonization, FAIR workflows, and reproducible pipelines

• Data resource papers with curated diabetes datasets
• Short communications reporting high impact findings
• Clinical informatics briefs or real world evidence analyses
• Perspectives on emerging bioinformatics challenges

Successful submissions present a clear research question, robust data provenance, and transparent validation. We value studies that demonstrate reproducibility and clinical relevance for diabetes or metabolic care.

Authors should explain novelty, document pipelines, and connect findings to biological mechanisms or clinical decisions.

• Clear data sources, preprocessing, and quality control
• Validation against independent cohorts or benchmarks
• Transparent model evaluation metrics and limitations
• Interpretation that links computation to diabetes outcomes

Submissions undergo editorial screening for scope fit, study design quality, and data transparency. Qualified manuscripts move to single blind peer review with subject matter experts.

JBD operates under an open access model to ensure diabetes bioinformatics research is discoverable and reusable. APCs are applied after acceptance and support peer review, production, and archiving services.

Membership options and affordable APC waivers are available for eligible authors. Contact the editorial office at [email protected] for guidance.

• Scope fit confirmed for bioinformatics and diabetes
• Data availability statement included
• Validation metrics reported
• Code or pipeline documentation prepared
• Cover letter explains novelty and impact

• 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.

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.