Data alchemy, from lab to insight: Transforming in vivo experiments into data science gold.

The article discusses the challenges and best practices of using in vivo-generated data in data science applications, such as statistical analyses and machine learning. It emphasizes the importance of aggregating and organizing data from multiple studies, normalizing the data to reduce variability, and transforming serial data into discrete values. The article also highlights the need for domain expertise in determining biological relevance and selecting appropriate ML algorithms. It concludes by advocating for the sharing of aggregated data sets to promote transparency, reproducibility, and scientific advancement. [Extracted from the article]