Aims & Scope
Journal of Medical Informatics and Decision Making (JMID) publishes computational methods, algorithms, and data science innovations that advance the analysis, interpretation, and management of biomedical data. We focus on the development and validation of informatics tools, not their clinical application.
Core Research Domains
Sequence Analysis & Genomics
- Genome assembly and annotation algorithms
- Sequence alignment and comparison methods
- Variant calling and interpretation pipelines
- Phylogenetic analysis and evolutionary modeling
- Metagenomics and microbiome data analysis
- RNA-seq and transcriptome analysis methods
"A novel graph-based algorithm for de novo genome assembly that reduces computational complexity by 40% while maintaining accuracy comparable to existing methods."
Structural Bioinformatics
- Protein structure prediction and modeling
- Molecular docking and binding site analysis
- Protein-protein interaction prediction
- Structural alignment algorithms
- Molecular dynamics simulation methods
- Drug-target interaction prediction
"Machine learning framework for predicting protein-ligand binding affinity using 3D structural features and physicochemical properties."
Biological Data Mining
- Gene expression data analysis methods
- Clustering and classification algorithms
- Feature selection and dimensionality reduction
- Network analysis and pathway enrichment
- Biomarker discovery algorithms
- Multi-omics data integration methods
"Deep learning approach for identifying cancer subtypes from multi-omics data with improved clustering accuracy and biological interpretability."
Biological Databases & Tools
- Database design and implementation
- Data standardization and ontology development
- Query optimization and retrieval systems
- Biological data visualization tools
- Workflow management systems
- Software packages for bioinformatics analysis
"Web-based platform for interactive visualization and analysis of single-cell RNA-seq data with real-time processing capabilities."
Secondary Focus Areas
Machine Learning for Biology
Novel machine learning architectures, deep learning models, and artificial intelligence methods specifically designed for biological data analysis. Focus on algorithmic innovation, not clinical prediction. Examples: neural networks for protein function prediction, reinforcement learning for molecular design, transfer learning for cross-species genomics.
Systems Biology & Network Analysis
Computational approaches to modeling biological systems, including metabolic networks, gene regulatory networks, and signaling pathways. Graph algorithms, network inference methods, and dynamic modeling techniques. Examples: Boolean network modeling, flux balance analysis, network motif discovery.
Image Analysis & Microscopy
Computational methods for analyzing biological images, including cell segmentation, object tracking, and feature extraction. Computer vision algorithms applied to microscopy, histopathology, or medical imaging data. Focus on algorithmic development, not diagnostic interpretation.
Text Mining & Literature Analysis
Natural language processing methods for extracting information from biomedical literature, clinical notes, or scientific databases. Named entity recognition, relation extraction, and knowledge graph construction. Examples: automated literature curation, drug-disease association mining.
Emerging Research Areas
Explicitly Out of Scope
We Do NOT Consider
Studies evaluating treatment effectiveness, patient outcomes, clinical decision-making, or healthcare delivery. These belong in clinical journals, not computational biology venues.
Implementation studies of EHR systems, telemedicine platforms, or hospital information systems without novel computational methods. Focus must be on algorithms, not system deployment.
Healthcare cost analysis, policy evaluation, resource allocation studies, or health economics research. These lack the computational focus required for bioinformatics.
Individual case reports, disease prevalence studies, epidemiological surveys, or clinical observations without computational method development. Examples: tuberculosis treatment outcomes, sarcoidosis prevalence studies.
Curriculum development, educational program evaluation, or training studies without novel computational pedagogy or tool development.
Scope Boundary
Translational computational studies leveraging preclinical or biological datasets for algorithm validation are considered when they advance methodological understanding and demonstrate clear generalizable computational innovation.
Article Types & Editorial Priorities
Preferred Article Types
- Original Research Articles
- Methods & Algorithms
- Software & Tools
- Database & Resources
- Systematic Reviews
Considered Article Types
- Short Communications
- Data Notes
- Benchmark Studies
- Application Notes
- Perspectives
Rarely Considered
- Opinion Pieces
- Commentaries
- Letters to Editor
- Conference Reports
- Book Reviews
Editorial Standards & Requirements
Reporting Guidelines
- Algorithm descriptions must be reproducible
- Code availability required for computational methods
- Benchmark datasets must be publicly accessible
- Performance metrics clearly defined and justified
- Statistical methods appropriately applied
Data & Code Policy
- Source code deposited in public repositories (GitHub, GitLab)
- Data shared via established repositories (GEO, SRA, PDB)
- Software tools documented with user manuals
- Version control and DOI assignment required
- Open source licensing encouraged
Ethics & Compliance
- Human data: IRB approval and informed consent
- Animal studies: IACUC approval and ARRIVE guidelines
- Competing interests disclosure required
- Funding sources must be declared
- AI-generated content must be disclosed
Preprint & Prior Publication
- Preprints on arXiv, bioRxiv accepted
- Conference abstracts do not preclude submission
- No duplicate publication allowed
- Preprint DOI must be disclosed at submission
- Significant expansion beyond preprint required
Editorial Decision Metrics
Ready to Submit Your Research?
If your work focuses on computational methods, algorithm development, or data science innovations for biological data analysis, we want to hear from you. Review our author guidelines and submit your manuscript today.
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