1. A novel translational bioinformatics framework for facilitating multimodal data analyses in preclinical models of neurological injury.
- Author
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Gaudio HA, Padmanabhan V, Landis WP, Silva LEV, Slovis J, Starr J, Weeks MK, Widmann NJ, Forti RM, Laurent GH, Ranieri NR, Mi F, Degani RE, Hallowell T, Delso N, Calkins H, Dobrzynski C, Haddad S, Kao SH, Hwang M, Shi L, Baker WB, Tsui F, Morgan RW, Kilbaugh TJ, and Ko TS
- Subjects
- Animals, Humans, Data Analysis, Brain Injuries, Traumatic, Computational Biology methods, Translational Research, Biomedical methods, Disease Models, Animal
- 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., Competing Interests: Declarations. Competing interests: The authors declare no competing interests., (© 2024. The Author(s).)
- Published
- 2024
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