Zifo and Maze Therapeutics are partnering to develop solutions to manage, store, and scale massive and often disparate biobank data. As an enabler of artificial intelligence (AI) and data-driven enterprise informatics, Zifo will deploy AI-enabled workflows in the process.

A Maze of Biobank Data

According to the newly formed partnership, using AI will address inherent issues with manual effort and data fragmentation. Currently, the manual process can be disjointed. Researchers struggle with static tools that can be difficult to implement.

The AI workflows help facilitate Maze Therapeutics’ goal of precision medicine—while bigger biobanks redefine genetic and phenotypic dataset access. To name a couple, such biobanks include UK Biobank and the All of Us Research Program.

For smaller operations, researchers must often piece together different platforms and datasets to connect the dots. This can be a challenge when talking about distinguishing between genetic signals, biological pathways, and phenotypes.

Zifo Platform Explained

Sandor Szalma, Scientific Advisor and a Data Veteran in the Omics space at Zifo, said Zifo’s solution is dynamically integrated.

The platform integrates downstream analysis workflows, streamlining the journey from data ingestion to discovery. New biobank data—such as UKBB-Proteomics, AGD/NashBio, and G&H—can be accommodated. And a foundation is there for future AI solutions, especially in protein modeling and genotype phenotype mapping.

“We are solving a fundamental problem for the industry by offering a seamless journey through previously fragmented data,” Szalma said. “The ability to render summary statistics in under five seconds is a testament to the solution’s robust back-end engineering.”

This, in turn, accelerates decision-making and empowers scientists to prioritize the science over the technology, according to Szalma.

“We have gained a truly scalable and robust solution for managing our biobank data,” said a Maze spokesperson. “The platform’s ability to handle diverse datasets and render results so quickly has been a game changer for our research.”