Aims & Scope
Aims
The Journal of Bioinformatics & Digital Health (BDH) aims to be the premier interdisciplinary platform for publishing innovative research at the intersection of computational intelligence, bioinformatics, and digital health systems. The journal aims to:
- Foster AI-driven discovery in biomedical informatics.
- Advance machine learning and deep learning methodologies for health data interpretation.
- Facilitate integration of AI and machine learning in personalized healthcare delivery.
- Advance the development of digital biomarkers, virtual health assistants, and explainable health models.
- Promote intelligent, explainable, and adaptive digital health platforms for real-world clinical deployment.
It is published quarterly online by Scilight Press.
Scope
The journal invites original research, reviews, and case studies in areas including but not limited to:
- AI in Bioinformatics and Systems Biology
- Deep learning for genomics, transcriptomics, proteomics
- Multi-modal fusion of omics, imaging, and EHR data
- AutoML and neural architecture search for bioinformatics pipelines
- Computational Intelligence in Digital Health
- Hybrid neuro-symbolic models for medical reasoning
- Fuzzy systems, evolutionary algorithms for diagnostic modeling
- Digital health twins and computational avatars
- Real-time AI for wearable and mobile health analytics
- Trustworthy and Explainable Health AI
- Causal AI and interpretable machine learning in clinical decisions
- Fairness, bias mitigation, and ethical implications in healthcare AI
- Federated and privacy-preserving AI in health data ecosystems
- Autonomous and Adaptive Health Systems
- Conversational agents and chatbots for chronic disease management
- Edge computing and sensor fusion in Internet of Medical Things
- Adaptive behavior modeling for lifestyle and mental health support
- Reinforcement learning for personalized interventions
- Intelligent Infrastructure for Public Health and Policy
- AI for epidemic forecasting and population health modeling
- Decision intelligence in resource-constrained digital health deployments
- Simulation, agent-based models, and data analytics for public health