Life Sciences in the Era of AI
With pandemics on the horizon, lethal diseases without a cure, chronic diseases that diminish people’s quality of life, and healthcare costs spiraling, scientists in the life sciences are racing against time to develop disease prevention strategies and accelerate breakthroughs in treatment. AI is a new tool that is emerging in scientific research, but unlocking the immense potential of AI in healthcare is hampered by a critical bottleneck: lack of accessible health data or scientific study data.
Human health data or genetic data, obtained through healthcare procedures or clinical studies is often restricted in its use and protected from unauthorized access as it contains private and personally identifiable information. The National Institutes of Health has issued the NIH Data Management and Sharing Policy of 2023 which mandates data sharing in specific formats and with defined timelines, ensuring responsible access and maximizing the use of valuable research data.
While sharing valuable study data is crucial for advancing research, the associated tasks of management, curation, and sharing can be burdensome for principal investigators, often requiring significant time and resources, including potentially hiring dedicated data managers. The data is then shared in different public repositories, some of which are controlled access, with certain data also stored in a researcher’s environment and with specific use restrictions. Publicly available data are often of minimal value for re-analysis if not well-annotated and described. Even with FAIR data initiatives, with FAIR standing for findable, accessible, interoperable, and reusable, in many cases, the quality or reusability of the data is unknown.
Collaborations around FAIR and Analyzable Data
To address these limitations in data access, data quality control, and lack of incentive and funding for data sharing, Lifetime Omics is developing FAIRLYZ, a novel dataset profiling and registry solution for FAIR and analyzable data, which has the triple objective:
- Enable safe and reproducible data sharing that includes data, metadata, and analysis tools with quality control of data.
- Help researchers comply with the 2023 NIH Data Management and Sharing policy.
- Give recognition to the investigators who collect and share the data and help them find funding and collaborations.
FAIRLYZ will link to datasets that include de-identified individual patient-level data with omics data and descriptions of analysis methods. FAIRLYZ will improve reproducibility and promote transparency of scientific studies and inform and accelerate new clinical research.
FAIRLYZ strives to make all scientific knowledge readily available and effortlessly integrated. With FAIRLYZ, we transform the scattered puzzle of research into a collaborative masterpiece, unlocking the potential for groundbreaking discoveries and a deeper understanding of the biological universe that affects our health and well-being.