A Dynamic Network Approach for the Study of Human Phenotypes

By César A. Hidalgo, Nicholas Blumm, Albert-László Barabási, Nicholas A. Christakis. PLoS Comput. Biol., 2009

676 citations recorded by the source corpus.

The use of networks to integrate different genetic, proteomic, and metabolic datasets has been proposed as a viable path toward elucidating the origins of specific diseases. Here we introduce a new phenotypic database summarizing correlations obtained from the disease history of more than 30 million patients in a Phenotypic Disease Network (PDN). We present evidence that the structure of the PDN is relevant to the understanding of illness progression by showing that (1) patients develop diseases close in the network to those they already have; (2) the progression of disease along the links of the network is different for patients of different genders and ethnicities; (3) patients diagnosed with diseases which are more highly connected in the PDN tend to die sooner than those affected by less connected diseases; and (4) diseases that tend to be preceded by others in the PDN tend to be more connected than diseases that precede other illnesses, and are associated with higher degrees of mortality. Our findings show that disease progression can be represented and studied using network methods, offering the potential to enhance our understanding of the origin and evolution of human diseases. The dataset introduced here, released concurrently with this publication, represents the largest relational phenotypic resource publicly available to the research community.

Subfields: Molecular Biology

View Paper: https://www.semanticscholar.org/paper/88566678193ef44d5a62a8b8371ed9027f353e71

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Citations in the corpus

Structure and dynamics of molecular networks: A novel paradigm of drug discovery: A comprehensive review (2012, background): “…number of data categories are related to the concept of disease enabling the construction of a large variety of networks (Goh et al., 2007; Rzhetsky et al., 2007; Feldman et al., 2008; Spiró et al., 2008; Hidalgo et al., 2009; Barabási et al., 2011; Zhang et al., 2011a; Janjic & Przulj, 2012).”
Mining electronic health records: towards better research applications and clinical care (2012, background): “Establishing patterns of directionality in comorbidity and disease progression is a first step towards using EHRs for predictive purposes, and this has been explored in network analysis studies of Medicare data 42,58 .”
Network-based methods for human disease gene prediction. (2011, background): “A similar disease phenotype network was constructed by connecting diseases based on their co-occurrences in a large number of patients [48].”
Using Electronic Patient Records to Discover Disease Correlations and Stratify Patient Cohorts (2011, methodology): “This comorbidity measure is very similar to the one used by Hidalgo et al. [66].”
Large-Scale Discovery of Disease-Disease and Disease-Gene Associations (2016, background): “Follow up studies by Hidalgo et al. 26 proposed human phenotypic networks (commonly referred to as comorbidity networks) to map with disease networks derived from EHR datasets, which were shown to successfully associate a higher connectivity of diseases with higher mortality.”
Human symptoms–disease network (2014, background): “Hidalgo et al. 9 constructed a disease phenotypic network using comorbidity patterns from more than 30 million Medicare patients, capturing disease progression patterns, such as that patients tend to develop diseases in the network vicinity of diseases that they already have and that patients with h”
Social contagion theory: examining dynamic social networks and human behavior (2011, neutral): “We have developed and placed into the public domain much network data and code (including for the Facebook network [2], biological networks [91], experimental networks [6], and various political network datasets [9295], and we have promptly shared our code and supplementary results with anyone who h”
MalaCards: an amalgamated human disease compendium with diverse clinical and genetic annotation and structured search (2016, methodology): “These diseases are identified with ICD9 codes in the PDN.”