1556-SecAct: Inferring Secreted Protein Signaling Activities
The paper introduces SecAct, a novel computational framework designed to infer the signaling activities of over 1,100 human secreted proteins. Traditional methods for quantifying these proteins are limited by narrow scope or non-human data, but this new model utilizes spatial transcriptomics from 1,258 tumor samples to map intercellular communication. By analyzing the spatial correlation between proteins and their target genes, SecAct accurately predicts treatment outcomes and biological responses across spatial, single-cell, and bulk datasets. The research validates the tool's effectiveness…
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