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Yet, very few of such differentially expressed genes are detectably perturbed in individual patients.Here, we develop a framework to construct personalized perturbation proﬁles for individual subjects, identifying the set of genes that are signiﬁcantly perturbed in each individual.
We show that this new notion substantially improves the predictive power of widely used interaction models.
The developed framework opens up the possibility to apply gene expression data in the context of precision medicine, with important implications for biomarker identiﬁ cation, drug development, diagnosis and treatment.
Background Deep mining of healthcare data has provided maps of comorbidity relationships between diseases.
Results Since Chronic Obstructive Pulmonary disease (COPD) has emerged as a central hub in temporal comorbidity networks, we developed a systematic integrative data-driven framework to identify shared disease-associated genes and pathways, as a proxy for the underlying generative mechanisms inducing comorbidity.
We integrated records from approximately 13 M patients from the Medicare database with disease-gene maps that we derived from several resources including a semantic-derived knowledge-base.
Link temporality has been shown to hinder many dynamical processes, from information spreading to accessibility, by disrupting network paths.