Aims & Scope
Journal of Bioinformatics and Diabetes (JBD) publishes computational methods, algorithms, and data-driven approaches that advance the molecular understanding and predictive modeling of diabetes through bioinformatics, systems biology, and machine learning.
Scope Structure
Tier 1: Core DomainsComputational Genomics & Genetics
- Genome-wide association studies (GWAS) for diabetes susceptibility
- Variant annotation and functional prediction algorithms
- Polygenic risk score development and validation
- Gene regulatory network inference
- Epigenomic data analysis (methylation, histone modifications)
- Population genetics and evolutionary analysis
Novel algorithm for identifying diabetes-associated regulatory variants using multi-omics integration and deep learning
Systems Biology & Network Analysis
- Metabolic pathway modeling and flux analysis
- Protein-protein interaction network construction
- Gene co-expression network analysis
- Dynamic systems modeling of glucose homeostasis
- Multi-scale biological network integration
- Network-based drug target identification
Systems-level analysis of insulin signaling pathways using constraint-based metabolic modeling
Machine Learning & Predictive Modeling
- Diabetes risk prediction algorithms
- Deep learning for omics data classification
- Feature selection and dimensionality reduction methods
- Time-series analysis of continuous glucose monitoring data
- Natural language processing for diabetes literature mining
- Ensemble methods for biomarker discovery
Convolutional neural network for predicting glycemic response from multi-modal sensor data
Omics Data Integration & Analysis
- Multi-omics data fusion strategies
- Transcriptomics analysis (RNA-seq, single-cell sequencing)
- Proteomics and post-translational modification analysis
- Metabolomics data processing and pathway enrichment
- Microbiome composition analysis and metagenomics
- Integrative analysis pipelines and workflows
Integrative framework combining transcriptomics and metabolomics to identify diabetes subtypes
Structural Bioinformatics
Protein structure prediction, molecular docking studies for diabetes-related proteins, and structure-function relationship analysis using computational methods.
Database Development
Curation and development of diabetes-specific databases, data standardization frameworks, and ontology development for diabetes research.
Sequence Analysis
Novel algorithms for sequence alignment, motif discovery in diabetes-related genes, and comparative genomics approaches.
Pharmacogenomics
Computational prediction of drug response variability, drug-gene interaction analysis, and personalized medicine algorithms for diabetes therapeutics.
Biostatistics & Study Design
Novel statistical methods for diabetes research, power analysis tools, meta-analysis frameworks, and reproducibility assessment methods.
Data Visualization
Interactive visualization tools for complex diabetes datasets, novel graphical representations of multi-dimensional data, and visual analytics platforms.
Artificial Intelligence Ethics
Computational frameworks for bias detection in diabetes prediction models, fairness-aware machine learning algorithms, and privacy-preserving data analysis methods.
Quantum Computing Applications
Quantum algorithms for molecular simulation, quantum machine learning for diabetes data analysis, and quantum-enhanced optimization methods.
Federated Learning
Distributed machine learning frameworks for multi-institutional diabetes data, privacy-preserving collaborative analysis, and decentralized model training.
Digital Twin Modeling
Computational models simulating individual patient physiology, personalized glucose dynamics prediction, and in silico clinical trial frameworks.
Article Types & Priorities
Expedited Review (14-21 days)
Regular Review (28-42 days)
Selective Review
Editorial Standards & Requirements
Reporting Guidelines
CONSORT-AI for AI/ML studies, TRIPOD for prediction models, STARD for diagnostic algorithms
Data Availability
Code and data must be deposited in public repositories (GitHub, Zenodo, figshare) with DOI
Ethics Compliance
IRB approval required for human data; adherence to GDPR, HIPAA for privacy-sensitive datasets
Preprint Policy
Preprints on arXiv, bioRxiv, medRxiv accepted; must be disclosed during submission
Reproducibility
Computational workflows must include version numbers, random seeds, and environment specifications
Open Science
Encouragement of open-source software, open data, and transparent peer review options
