Vicky Yao - Quantitative Biology Seminar Series

Talk Title: Integrative computational approaches for modeling complex disease biology across scales and systems; Abstract: To effectively model the molecular underpinnings of complex traits and diseases, computational methods must integrate diverse data types, handle partial or limited observations, and remain robust to variations in dataset size. In this talk, I will present several recent methods we have developed to address these challenges across diverse studies, assay types, and organisms, leveraging novel statistical and machine learning approaches. First, I will introduce ALPINE, an NMF-based framework that disentangles the influence of technical and non-relevant phenotypic factors in single-cell transcriptomic data, enabling the integration of multiple studies. Integrating across data types, I will discuss our method, seismic, which combines genome-wide association studies with single-cell RNA sequencing to prioritize disease-relevant cell types, linking genetic variation to cellular function. Finally, I will discuss ETNA, a language translation-inspired approach that embeds protein-protein interaction networks from different organisms into a shared space, facilitating cross-species functional comparisons. Together, these methods highlight how diverse data sources can be integrated across molecular, cellular, and organism levels to better model complex disease biology. The Faculty of Computing & Data Sciences (CDS) and the BU Bioinformatics Program are proud to announce the launch of the Quantitative Biology Seminar Series. This intersectional seminar series aims to foster connections between various communities, including computationalists, quantitative experimentalists, and theorists, each with their own broad range of interests. Registration required.