Related Experiment Video
Updated: May 8, 2025
Lipidomics and Transcriptomics in Neurological Diseases
Published on: March 18, 2022
A novel translational bioinformatics framework for facilitating multimodal data analyses in preclinical models of
Hunter A Gaudio1, Viveknarayanan Padmanabhan2, William P Landis1
1Resuscitation Science Center and Department of Anesthesiology and Critical Care Medicine, Children's Hospital of Philadelphia, Philadelphia, PA, 19104, USA.
Insights
A new data framework aids pediatric neurological injury research by standardizing multimodal data. This improves data management and analysis for developing crucial monitoring technologies and therapeutics.
Area of Science:
- Neuroscience
- Biomedical Engineering
- Data Science
Background:
- Pediatric neurological injury and disease pose significant public health challenges.
- Advancements in survival rates for primary injuries necessitate better management of secondary neurological injury.
- Current translational research is hampered by a lack of standardized data management frameworks for complex datasets.
Purpose of the Study:
- To present a generalizable data framework for managing, processing, and analyzing multimodal datasets in preclinical pediatric neurological injury research.
- To address the technological gap in data management for translational research.
- To facilitate the development of monitoring technologies and therapeutics for secondary neurological injury.
Main Methods:
- Implementation of a custom, interactive data framework for large animal research.
- Development of a dashboard for exploratory data analysis and dataset download.
- Integration of various data types including single measure, repeated measures, time series, and imaging data.
Main Results:
- The framework enables effective management of diverse data types (single measure, repeated measures, time series, imaging).
- It facilitates dataset integration for cross-experimental model, cohort, and group comparisons.
- A predictive model development use case demonstrated the framework's utility and value.
Conclusions:
- The presented data framework enhances data management and analysis capabilities for preclinical research.
- It supports the integration of heterogeneous datasets, crucial for advancing pediatric neurological injury research.
- The framework serves as a scalable template for translational research labs requiring dynamic data platforms.
Abstract:
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 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 datasets. 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 a custom, interactive dashboard for exploratory analysis and filtered dataset download. Compared with existing clinical and preclinical data management solutions, the presented framework better enables management of various data types (single measure, repeated measures, time series, and imaging), integration of datasets for comparison across experimental models, cohorts, and groups, and facilitation of predictive modeling from integrated datasets. Further, a predictive model development use case demonstrated utilization and value of the data framework. The general outline of a preclinical data framework presented here can serve as a template for other translational research labs that generate heterogeneous datasets and require a dynamic platform that can easily evolve alongside their research.
