Functional heterogeneity of healthy human tissues complicates interpretation of molecular studies, impeding precision therapeutic target identification and treatment. Considering this, we generated a graph neural network with Reactome-based architecture and trained it using 9,115 samples from Genotype-Tissue Expression (GTEx). Our graph neural network (GNN) achieves adjusted Rand index (ARI) = 0.
View Article and Find Full Text PDFConsidering protein mutations in their biological context is essential for understanding their functional impact, interpretation of high-dimensional datasets and development of effective targeted therapies in personalized medicine. We combined the curated knowledge of biochemical reactions from Reactome with the analysis of interaction-mediating 3D interfaces from Mechismo. In addition, we provided a software tool for users to explore and browse the analysis results in a multi-scale perspective starting from pathways and reactions to protein-protein interactions and protein 3D structures.
View Article and Find Full Text PDFThe response to respiratory viruses varies substantially between individuals, and there are currently no known molecular predictors from the early stages of infection. Here we conduct a community-based analysis to determine whether pre- or early post-exposure molecular factors could predict physiologic responses to viral exposure. Using peripheral blood gene expression profiles collected from healthy subjects prior to exposure to one of four respiratory viruses (H1N1, H3N2, Rhinovirus, and RSV), as well as up to 24 h following exposure, we find that it is possible to construct models predictive of symptomatic response using profiles even prior to viral exposure.
View Article and Find Full Text PDFHigh lethality rates associated with metastatic cancer highlight an urgent medical need for improved understanding of biologic mechanisms driving metastatic spread and identification of biomarkers predicting late-stage progression. Numerous neoplastic cell intrinsic and extrinsic mechanisms fuel tumor progression; however, mechanisms driving heterogeneity of neoplastic cells in solid tumors remain obscure. Increased mutational rates of neoplastic cells in stressed environments are implicated but cannot explain all aspects of tumor heterogeneity.
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