Molecular Cell Biomechanics Laboratory
University of California, Berkeley
Modeling how physical forces shape living systems
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Latest Publication
SIMBA-GNN: mechanistic graph learning for microbiome prediction
Predicting how gut microbial communities assemble and change requires models that capture the underlying mechanisms driving interspecies interactions, not just taxonomic correlations. We present SIMBA, a simulation-augmented graph neural network that integr...
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Recent Publication
Semi-supervised Retrieval of Functional Residues Through the Integration of Protein Language Models and Gene Ontology Data
Abstract Motivation Experimental studies of protein function often focus on mechanistic descriptions, characterizing how specific sites and residues contribute to activity. Abstractions such as domains and active sites enable quantitative descriptions of ho...
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