Implementation of a Product Lifecycle Management System for Biomedical Research

Today's biomedical research shows similar characteristics with manufacturing industry 20 years ago. Biomedical data are more and more complex and heterogeneous due to recent scientific discoveries and technological innovations. In a previous work, the Product Lifecycle Management (PLM) paradigm was applied to neuroimaging research to manage heterogeneous data and their provenance. A BMI-LM data model and an associated platform were proposed to enable data reuse and sharing in neuroimaging. Here, this application is extended to biomedical data related to Histology, Proteomics, and PET-CT modalities. Data originate from the IVIR laboratory in preclinical research of the Paris Cardiovascular Research Center (PARCC). We proposed a data and workflow integration method and applied them to the collected preclinical data. An extended version of the data model, that we called BMS-LM for BioMedical Study - Lifecycle Management, was developed. Three concepts were added to the data model: Agent, Sample and Intervention. As a proof of concept, samples of imported data from the three identified modalities are given. Results support the use of the BMS-LM data model and system to ameliorate trust of biomedical data and prospectively their reuse.

Automated summary

Today's biomedical research data are becoming more complex and heterogeneous, requiring the application of a product lifecycle management paradigm to manage and share these data. In this study, we extended a previous application in neuroimaging research to include histology, proteomics, and PET-CT modalities in biomedical data. By introducing the concepts of Agent, Sample, and Intervention, we developed an extended data model called BMS-LM, and demonstrated its effectiveness in facilitating data reuse.