Two-Part Webinar Series 

Technologies for analyzing proteome, metabolome, transcriptome, and epigenome data at both spatial and single-cell levels have come a long way. Taken together, these methods provide a holistic view of biological processes and systems and reveal potential therapeutic targets. However, combining and integrating data from these diverse modalities and platforms presents a significant challenge.

In this two-part GEN webinar series, our expert speakers Garry Nolan, PhD, (Part 1) will discuss MaxFuse, a new AI algorithm that provides reliable, fast, and cost-effective integration of spatial proteomic data with single-cell transcriptomic and epigenomic datasets. In part two, hear from Alexandro (Alex) Trevino, PhD, on how AI-powered algorithms within the Enable platform can facilitate comprehensive data storage, annotation, analysis, and search.

Webinar Part 1: Bridging Spatial Genomics and Spatial Proteomics with MaxFuse, a New AI-Powered Algorithm  

Single-cell sequencing and spatial omics technologies can now profile diverse molecular readouts within cells and preserve their spatial context. However, integrating data across modalities (“cross-modal integration”) remains challenging, especially when the linked features between modalities are weak or uninformative. In this webinar, Garry Nolan introduces MaxFuse, a novel cross-modal data integration method that overcomes these limitations. MaxFuse enables the spatial consolidation of proteomic, transcriptomic, and epigenomic information at single-cell resolution opening exciting possibilities for dissecting complex biological processes.

Interested in Part Two: Data Integration and Beyond: Creating and Leveraging Biological Atlases of Disease to Guide Drug Discovery – Watch Here>>