A dynamic network approach for the study of human phenotypes.

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...

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Autores principales:	César A Hidalgo, Nicholas Blumm, Albert-László Barabási, Nicholas A Christakis
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2009
Materias:	Biology (General) QH301-705.5
Acceso en línea:	https://doaj.org/article/dd9bff17bc4b454a8082ee99ba3df3cb
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spelling	oai:doaj.org-article:dd9bff17bc4b454a8082ee99ba3df3cb2021-11-25T05:41:44ZA dynamic network approach for the study of human phenotypes.1553-734X1553-735810.1371/journal.pcbi.1000353https://doaj.org/article/dd9bff17bc4b454a8082ee99ba3df3cb2009-04-01T00:00:00Zhttps://www.ncbi.nlm.nih.gov/pmc/articles/pmid/19360091/?tool=EBIhttps://doaj.org/toc/1553-734Xhttps://doaj.org/toc/1553-7358The 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.César A HidalgoCésar A HidalgoNicholas BlummAlbert-László BarabásiNicholas A ChristakisPublic Library of Science (PLoS)articleBiology (General)QH301-705.5ENPLoS Computational Biology, Vol 5, Iss 4, p e1000353 (2009)
institution	DOAJ
collection	DOAJ
language	EN
topic	Biology (General) QH301-705.5
spellingShingle	Biology (General) QH301-705.5 César A Hidalgo César A Hidalgo Nicholas Blumm Albert-László Barabási Nicholas A Christakis A dynamic network approach for the study of human phenotypes.
description	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.
format	article
author	César A Hidalgo César A Hidalgo Nicholas Blumm Albert-László Barabási Nicholas A Christakis
author_facet	César A Hidalgo César A Hidalgo Nicholas Blumm Albert-László Barabási Nicholas A Christakis
author_sort	César A Hidalgo
title	A dynamic network approach for the study of human phenotypes.
title_short	A dynamic network approach for the study of human phenotypes.
title_full	A dynamic network approach for the study of human phenotypes.
title_fullStr	A dynamic network approach for the study of human phenotypes.
title_full_unstemmed	A dynamic network approach for the study of human phenotypes.
title_sort	dynamic network approach for the study of human phenotypes.
publisher	Public Library of Science (PLoS)
publishDate	2009
url	https://doaj.org/article/dd9bff17bc4b454a8082ee99ba3df3cb
work_keys_str_mv	AT cesarahidalgo adynamicnetworkapproachforthestudyofhumanphenotypes AT cesarahidalgo adynamicnetworkapproachforthestudyofhumanphenotypes AT nicholasblumm adynamicnetworkapproachforthestudyofhumanphenotypes AT albertlaszlobarabasi adynamicnetworkapproachforthestudyofhumanphenotypes AT nicholasachristakis adynamicnetworkapproachforthestudyofhumanphenotypes AT cesarahidalgo dynamicnetworkapproachforthestudyofhumanphenotypes AT cesarahidalgo dynamicnetworkapproachforthestudyofhumanphenotypes AT nicholasblumm dynamicnetworkapproachforthestudyofhumanphenotypes AT albertlaszlobarabasi dynamicnetworkapproachforthestudyofhumanphenotypes AT nicholasachristakis dynamicnetworkapproachforthestudyofhumanphenotypes
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A dynamic network approach for the study of human phenotypes.

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