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A Template for Translational Bioinformatics: Facilitating Multimodal Data Analyses in Preclinical Models of
Biorxiv : the Preprint Server for Biology
|July 28, 2023
Summary
A new data framework aids pediatric neurological research by managing diverse data types from preclinical studies. This approach supports the development of predictive models for critical care outcomes.
Area of Science:
- Neurology
- Biomedical Engineering
- Data Science
Background:
- Pediatric neurological injury and disease pose significant public health challenges, exacerbated by rising survival rates from primary injuries and a lack of effective monitoring and therapeutic strategies for secondary injury.
- Translational preclinical research is vital for developing solutions but is hampered by inadequate data frameworks and standards for managing, processing, and analyzing complex, multimodal datasets.
Approach:
- A generalizable data framework was developed and implemented for large animal research at the Children's Hospital of Philadelphia to overcome existing technological limitations.
- The framework integrates heterogeneous data types, including single measure, repeated measures, time series, and imaging data, across various experimental models.
- It culminates in an interactive dashboard for exploratory data analysis and filtered dataset downloads, facilitating dynamic visualization of integrated datasets.
Key Points:
- The framework accommodates diverse data types and integrates datasets across different experimental models, surpassing limitations of existing clinical and preclinical data management solutions.
- It enables dynamic visualization of integrated datasets, crucial for understanding complex biological processes.
- A use case demonstrates its application in developing predictive models for intra-arrest prediction of cardiopulmonary resuscitation outcomes.
Conclusions:
- The presented preclinical data framework offers a flexible and scalable solution for managing heterogeneous datasets in translational research.
- It can serve as a template for other research labs requiring dynamic data management platforms that can adapt to evolving research needs.
- This framework has the potential to accelerate the development of novel diagnostics and therapeutics for pediatric neurological conditions.
