Molecular Data Science
Volume • Issue
- ISSN: 2590-0633
The vast accumulation of health-related and biomedical data resources and the rapid proliferation of technological developments in data analytics are opening up new avenues to ga… Read more
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Request a sales quoteThe vast accumulation of health-related and biomedical data resources and the rapid proliferation of technological developments in data analytics are opening up new avenues to gaining insight in complex biological processes. High-throughput and precision measurement technologies are generating at a rapid pace large multimodal data sets that are distributed globally. However, new methods and infrastructures to extract, pool, integrate, refine, store, secure, analyse and visualize data are needed to unleash the power of these data resources, while tools and workflows should be made more accessible and easier to use by researchers and the public at large.
Management and stewardship of life science and health data resources and subsequent analytics come with distinct domain challenges: Increasingly, a complex molecular focus requires joint expertise in the life science and health domain and data science domain, often requiring translation between the two in order to foster technology push and pull. Molecular science is done in ever larger domain consortia, where the emergence of community embraced standards is essential.
The journal invites data science contributions covering foundational and theoretical research, platforms, infrastructure, methods, applications, and tools in molecular life sciences and biomedicine. We also welcome contributions aimed at fostering community engagement and agile best-practice development. Various submission types are facilitated (research/use case/infrastructure long articles; white paper/best practices/education short articles). As the remit of the journal is molecular life sciences and health, manuscripts must revolve around a central biological question.
Data Science challenges include:
Reproducibility
Experimental design
Data analytics
Scalability
Privacy
Communities include those in
Metabolic diseases
Cancer
Infectious diseases
Neurodegenerative diseases
Nutrition
Topics within the scope of the journal include (but are not limited to) the design, development, evaluation or validation of the following data science technology aspects in molecular life science and health:
DATA SCIENCE TECHNOLOGY
Data management and stewardship
Data curation
FAIR data principles
Data integration
Research data publication, quality, indexing and discovery
Infrastructure development
Privacy-aware analytics
Machine learning, deep learning
Natural language processing and text-mining
Semantics
Trend discovery and analysis
Graph mining and knowledge extraction
Social and wearable sensors
Scientific web services and executable workflows
MOLECULAR LIFE SCIENCE AND HEALTH REMIT
Integration of omics, biomedical, nutrition, lifestyle and/or social data and subsequent analytics
Multi-level integration of molecular processes
Personalized and precision medicine
Disease diagnosis, prognosis and prognostics
- ISSN: 2590-0633