Meet us at BioTechX Basel 2023

Translational medicine and AI-based drug discovery need a powerful database system

We are now in the era of data-driven medicine, and generative AI is completely changing the game for life sciences.

Organizations are in a race to harness vast volumes of multi-omics data to deliver on the promise of bench-to-bedside medicine, be it newborn sequencing or development of new therapies. But traditional pipelines and tooling are falling short when it comes to the scale, speed, diversity and accuracy required in multi-omics research. In addition, the variety of tools used for each of the data modalities have created governance and discoverability pains for R&D IT and infrastructure teams.

TileDB is a novel multimodal database system that morphs to capture all the demanding use cases of life sciences. 10 of the 20 top pharmaceutical companies and a large number of biotechs who are pioneering AI-based drug development and precision medicine have embraced solutions built on TileDB. They are achieving unprecedented performance at Atlas- and Biobank-scale to rapidly deliver data products to bioinformaticians, clinicians and end-users across all omics domains.

Come see how TileDB tackles data challenges in population genomics, single-cell analysis and biomedical imaging.

Stavros will describe how TileDB solves the challenges posed by multi-omics use cases where diverse data modalities and workflows require large downloads; slowing progress. TileDB addresses these challenges through its morphing ability to capture all complex data modalities (genomics, single-cell, imaging, patient records, and more), its uber scalable architecture, and its native support for vector search and the ability to integrate with LLMs. The talk will conclude with case studies of how top life sciences companies are building powerful solutions with TileDB.

Imagine cutting down single-cell data workflows from hours to minutes.

TileDB and the single-cell biology team at the Chan Zuckerberg Initiative have partnered to develop SOMA: a unified and open-source solution for single-cell data. Paul will show how TileDB-backed SOMA datasets enable researchers to rapidly query and slice subsets of the data for analysis directly from the cloud, without downloading the entire dataset. He will demonstrate how the TileDB-SOMA R and Python packages integrate directly with popular analysis frameworks like Seurat and Scanpy. Lastly, he will explain how a unified and governed data analytics platform that provides collaboration and a serverless environment accelerates AI-based drug development.

CONFERENCE

The AI-ready database system for multi-omics

Book a meeting with us

Translational medicine and AI-based drug discovery need a powerful database system

Catch our talks at the conference

Multi-omics needs a multimodal, atlas-scale and AI-ready database system

Cloud-native single-cell data management: Powering the largest public single-cell repositories in the world

Not attending? We’re flexible.

Meet the team of TileDB experts

Stavros Papadopoulos

Seth Shelnutt

Mark Rubin

Jeremy Leipzig

Chad Krilow

Paul Hoffman