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A Template for Translational Bioinformatics: Facilitating Multimodal Data Analyses in Preclinical Models of
Insights
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.
Background:
Pediatric neurological injury and disease is a critical public health issue due to increasing rates of survival from primary injuries (e.g., cardiac arrest, traumatic brain injury) and a lack of monitoring technologies and therapeutics for the treatment of secondary neurological injury. Translational, preclinical research facilitates the development of solutions to address this growing issue but is hindered by a lack of available data frameworks and standards for the management, processing, and analysis of multimodal data sets.
Methods:
Here, we present a generalizable data framework that was implemented for large animal research at the Children's Hospital of Philadelphia to address this technological gap. The presented framework culminates in an interactive dashboard for exploratory analysis and filtered data set download.
Results:
Compared with existing clinical and preclinical data management solutions, the presented framework accommodates heterogeneous data types (single measure, repeated measures, time series, and imaging), integrates data sets across various experimental models, and facilitates dynamic visualization of integrated data sets. We present a use case of this framework for predictive model development for intra-arrest prediction of cardiopulmonary resuscitation outcome.
Conclusions:
The described preclinical data framework may serve as a template to aid in data management efforts in other translational research labs that generate heterogeneous data sets and require a dynamic platform that can easily evolve alongside their research.
