Person: Christakis, Nicholas A.
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Publication Viral Perturbations of Host Networks Reflect Disease Etiology
(Public Library of Science, 2012) Gulbahce, Natali; Yan, Han; Dricot, Amélie; Padi, Megha; Byrdsong, Danielle; Franchi, Rachel; Lee, Deok-Sun; Rozenblatt-Rosen, Orit; Mar, Jessica C.; Calderwood, Michael; Baldwin, Amy; Zhao, Bo; Santhanam, Balaji; Braun, Pascal; Simonis, Nicolas; Huh, Kyung-Won; Hellner, Karin; Grace, Miranda; Chen, Alyce; Rubio, Renee; Marto, Jarrod; Christakis, Nicholas A.; Kieff, Elliott; Roth, Fritz; Roecklein-Canfield, Jennifer; DeCaprio, James; Cusick, Michael; Quackenbush, John; Hill, David; Münger, Karl; Vidal, Marc; Barabási, Albert-LászlóMany human diseases, arising from mutations of disease susceptibility genes (genetic diseases), are also associated with viral infections (virally implicated diseases), either in a directly causal manner or by indirect associations. Here we examine whether viral perturbations of host interactome may underlie such virally implicated disease relationships. Using as models two different human viruses, Epstein-Barr virus (EBV) and human papillomavirus (HPV), we find that host targets of viral proteins reside in network proximity to products of disease susceptibility genes. Expression changes in virally implicated disease tissues and comorbidity patterns cluster significantly in the network vicinity of viral targets. The topological proximity found between cellular targets of viral proteins and disease genes was exploited to uncover a novel pathway linking HPV to Fanconi anemia.
Publication The Impact of Cellular Networks on Disease Comorbidity
(Nature Publishing Group, 2009) Park, Juyong; Lee, Deok-Sun; Christakis, Nicholas A.; Barabási, Albert-LászlóThe impact of disease-causing defects is often not limited to the products of a mutated gene but, thanks to interactions between the molecular components, may also affect other cellular functions, resulting in potential comorbidity effects. By combining information on cellular interactions, disease--gene associations, and population-level disease patterns extracted from Medicare data, we find statistically significant correlations between the underlying structure of cellular networks and disease comorbidity patterns in the human population. Our results indicate that such a combination of population-level data and cellular network information could help build novel hypotheses about disease mechanisms.
Publication Computational Social Science
(American Association for the Advancement of Science, 2009) Lazer, David; Pentland, Alex; Adamic, Lada; Aral, Sinan; Barabási, Albert-László; Brewer, Devon; Christakis, Nicholas A.; Contractor, Noshir; Fowler, James; Gutmann, Myron; Jebara, Tony; King, Gary; Macy, Michael; Roy, Deb; Van Alstyne, MarshallA field is emerging that leverages the capacity to collect and analyze data at a scale that may reveal patterns of individual and group behaviors.