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Making in vivo Data Actionable

As the complexity of in vivo data continues to expand, the integration of artificial intelligence has emerged as a groundbreaking catalyst for discovery.

Unleash the Power of Structured Data: Transforming In Vivo Insights with Artificial Intelligence

In the dynamic realm of scientific research, where rich and complex in vivo data is fundamental, artificial intelligence (AI) has the potential to be a catalyst for discovery. Harnessing the potential of structured data, this synergy promises to unlock new dimensions of understanding. Biopharma companies are betting on AI and other advanced analytics tools to extract meaningful insights from in vivo experiments. But exactly what does this transformative journey look like?

Today’s Data Challenge

The ability to reproduce and build future studies depends on data being robust and reliable. Yet research shows that a large percentage of in vivo data is irreproducible, with researchers finding that anything between 18-89% of biomedical research cannot be successfully reproduced. In fact, some put the potential economic cost of not being able to reproduce results at between $10 and $50 billion a year.

In a recent webinar sponsored by RockStep Solutions, panelists agreed that to leverage the full value of in vivo data it would be necessary to work in parallel with legacy systems and modern technology. They also pointed out that the fear of not achieving a return on investment underlies a hesitancy to move forward. This hesitancy, however, seems to be at odds with the reality of not being able to reproduce study results down the road, ultimately impacting timelines and cost.

Modern data management practices give scientists access to aggregated data in real-time. Connected data can be transformed into actionable insights, but most current technology cannot aggregate data across multiple legacy systems with enough accuracy and quality. In the RockStep webinar, panelists agreed that organizations will need to overcome resistance to change, while ensuring regulatory compliance with data collection and management, adaptable workflows, easy-to-navigate and configurable workflows, and the ability for researchers to quickly and securely access their data.

The Potential of AI, the Reality of Unstructured Data

The in vivo world presents researchers with challenges such as data complexity, variability, and sheer volume. Traditional methods of analysis often struggle to keep pace with the demands of processing large datasets generated from live experiments, particularly if the data is unstructured and collected through spreadsheets or other outdated methods. This is where AI comes in, offering a paradigm shift in the way we approach and derive insights from in vivo data. But deriving insights relies on the data being structured in a way that it can be used for advanced algorithms and machine learning models.

Structured data is organized in a highly defined manner, often presented in tables or databases, with clear relationships between different elements. Structured data allows AI systems to navigate through the data with precision, identifying patterns and relationships, enabling models to discern meaningful connections within the complex web of in vivo experiments. This capability empowers researchers to explore new avenues of inquiry, leading to a deeper understanding of novel biological pathways, potential therapeutic targets, and a deeper understanding of the intricacies of living organisms.

As “big data” becomes increasingly important to in vivo research, AI facilitates efficient and accurate analysis of data, empowering scientists to make decisions, and ultimately, get therapeutics to market faster.

Advantages of Applying Advanced Analytics with Structured Data


Enhanced Accuracy in Analysis.

Given the importance of structured data, the integration with AI must address current variability in data formats, collection methods, and experimental conditions as these variations pose a challenge for current AI models. This adds the serendipitous benefit of implementing standardization of scientific methods and data publications within and between research organizations. Ultimately, this endeavor leads to robust quality control measures, enhancing the reliability and reproducibility of findings.


Increased Efficiency in Data Processing.

Structured data, by its very design, facilitates streamlined data processing. AI systems, when applied to structured in vivo data, exhibit increased efficiency in handling and interpreting information. This efficiency translates into quicker analysis times and the ability to process large datasets at scale. Researchers can accelerate their investigations, allowing for a more agile and responsive approach to scientific inquiries.


Deeper Insights and Discoveries.

The marriage of structured data and AI unlocks the potential for uncovering previously hidden insights within in vivo datasets. AI algorithms excel at identifying complex relationships and trends, even in datasets with multiple variables. As the benefits of structured data become increasingly evident, organizations and researchers are recognizing that their ability to glean meaningful insights from in vivo experiments is transformative. The synergy between structured data and AI not only addresses the challenges posed by in vivo data but also elevates the quality and depth of insights derived from experiments.


Interdisciplinary Collaboration.

The standardization of structured data promotes interdisciplinary cooperation. Researchers, data scientists, and domain experts can work cohesively to bridge the gap between data generation, analysis, and interpretation. This holistic approach ensures that the insights derived from structured pre-clinical data align with the broader context of in vivo research and across the R&D lifecycle. There is no better use case than when researchers transform pre-clinical data into clinical trials. Structured clinical data, encompassing patient records, medical histories, and treatment outcomes, serve as a rich source for AI applications in personalized healthcare. The ability to analyze these structured data from seemingly disparate pre-clinical and clinical data enhances diagnostic accuracy and supports the development of personalized treatment plans and new therapies, ultimately improving patient outcomes.

Innovation Starts with High-Quality Data

Unlocking insights start with good data management practices, which would allow access to organized digital records and efficient extraction of clinically relevant metadata for analysis. As Chuck Donnelly, CEO and Co-Founder of RockStep Solutions, pointed out during the RockStep sponsored webinar, data management systems allow widespread access to organized digital records and efficient extraction of clinically relevant metadata for analysis. Historical data can be employed for virtual control groups in safety studies, while synthetic data fills knowledge gaps and reveals new nuances. Ultimately, the benefits of enhanced accuracy, increased efficiency, and deeper insights underscores the potential of this dynamic union to revolutionize scientific inquiry. Yet, it is crucial to acknowledge and navigate the challenges associated with data quality. The path forward requires a strategic approach and continued innovation to overcome these challenges and harness the full potential of structured data for AI-powered in vivo research.

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