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Empowering the Development of Precision Medicine through Analytics, Machine Learning and AI

To accelerate the discovery and development of the next generation of medicines, it is important to efficiently incorporate the outcomes of translational research into clinical studies, while transferring scientific insights from the clinic to the bench to guide further laboratory research. The goal of translational and exploratory clinical research is thereby to increase the probability of success of clinical trials through enabling understanding and prediction of drugs’ mechanism of action, potential side effects, and patient responses to treatment.

This approach presents significant logistic and technical challenges. Firstly, bioinformaticians, statisticians, and data scientists need to find the right data from within or across clinical and pre-clinical studies, stored in different locations and management systems. Once they do find the relevant data, they may not have permission to access it, since clinical data contains sensitive information about patients. It is also common that the available data is not in the appropriate format, thus requiring harmonization prior to analysis. After the data has been collected from different sources, processed, and harmonized, scientists need to integrate and potentially further harmonize multi-modal data for downstream analysis and visualization using a range of statistical and data visualization tools, such as RStudio. All these processes are highly time-consuming and can delay subsequent important business decisions.

What bioinformaticians, statisticians, and data scientists need is a platform that provides them with analysis-ready datasets. They would also vastly benefit from a tool that allows the communication of results to stakeholders (e.g., scientists and clinical researchers) in a standardized, reproducible, and interactive manner. This is where Genedata Profiler makes a difference.

The Advantages of Integrating RStudio with Genedata Profiler

FACILITATED SEARCH & EASY ACCESS TO ANALYSIS READY DATASETS
  • Genedata Profiler enables your organization to aggregate, harmonize, and structure data from different sources in a centralized repository.
  • It provides you with self-service access to data allowing you to rapidly query, select and condense billions of data points for analysis.
  • By integrating with RStudio or another 3rd party tool e.g., Jupyter, Genedata Profiler allows you to easily perform cross study and cross technology analyses, serving as a basis for advanced machine learning and AI applications.
  • Any changes you make to your data using RStudio or other 3rd party analytical tools are saved with a link to the source data for data provenance.
SECURE COLLABORATION ENVIRONMENT
  • Genedata Profiler allows you to maintain a high level of security by setting permissions, only allowing specific users to access specific types of data and perform specific activities.
  • Analysis-ready datasets are updated in real-time, including revocation of consent, providing you with real-time governance of sensitive patient information.
  • The entire system, including RStudio, can be implemented into a GxP validated environment.
  • As Genedata Profiler allows you to keep track of all changes made within RStudio, you and your team members will never lose a single line of code.
  • By generating RStudio interactive dashboards (RShiny) within Profiler, you can easily share results with partners e.g. clinical leads or other scientists, enabling them to further explore the data and draw actionable conclusions.
HIGH-PERFORMANT, FAST & SCALABLE INFRASTRUCTURE
  • Genedata Profiler allows you to import and process any amount of data, while maintaining optimal analytical performance.
  • Operating using a cloud infrastructure ensures accelerated data processing and analysis by multiple users as tasks are fully parallelized.
  • SQL over REST and predicate pushdown allows you to query, access and load only the data you need for analysis and visualization in RStudio. This not only allows you to save time but also prevents excessive strain on analytical tools.
  • Since computing resources are used exclusively on demand, elastically, this allows you to optimize your resources and cost.

Integrable RStudio Products