The human disease network

By Kwang-Il Goh, Michael E. Cusick, D. Valle, B. Childs, Marc Vidal, Albert-László Barabási. 2007

3,228 citations recorded by the source corpus.

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In Molecular Biology

Citations in the corpus

Computational solutions for omics data (2013, background): “The multifactorial nature of complex diseases suggests that although the genes underlying these diseases may differ among afflicted individuals, the pathways that are perturbed are likely to be shared, and thus proteins associated with the same disease have a tendency to interact 142 .”
Mapping complex disease traits with global gene expression (2009, background): “As perturbations of such networks underlie the pathogenesis of many disease”
Hierarchical block structures and high-resolution model selection in large networks (2013, neutral): “4 Disease Genes [77] 15 Internet AS (Caida)b 26 IMDB film-actorc [32] (bipartite)”
BIOLOGICAL NETWORK EXPLORATION WITH CYTOSCAPE 3 (2014, methodology): “In this protocol, we import data from the human disease network constructed by Goh et al. (Goh et al. , 2007) This study constructed a global association network between diseases and genes using curated mutation data from the Online Mendelian Inheritance in Man (OMIM) (Hamosh et al. , 2005) database”
HINT: High-quality protein interactomes and their applications in understanding human disease (2012, background): “Even though this result is unexpected in light of previous findings that interaction hubs are less likely to cause disease [21,22], it will help understand mechanisms of various disease processes and develop corresponding treatments.”
Multilevel Compression of Random Walks on Networks Reveals Hierarchical Organization in Large Integrated Systems (2010, background): “18) p 1 ˘ k1 i (n 1 n 2)hki : (19) The mixing parameters 1 and 2 are dened as follows: 1 2 1 = k3 i k3 i + k2 i + k1 i (20) 2 = k2 i k3 i + k2 i + k1 i (21) 1 = k1 i k3 i + k2 i + k1 i ; (22) such that nodes share on average a fraction 1 of their links with nodes in other modules, a fraction ”
Understanding multicellular function and disease with human tissue-specific networks (2015, neutral): “Most human diseases are syndromes with complex origins and manifestations in multiple tissues 4,26,46 .”
Human Disease-Drug Network Based on Genomic Expression Profiles (2009, result): “Integrating other results obtained via conceptually different approaches may also improve the reliability and sensitivity. For examples, we can add links (or modify scores) between two diseases that share at least one gene whose mutations are associated with both diseases”
Comprehensive Analysis of Tissue-wide Gene Expression and Phenotype Data Reveals Tissues Affected in Rare Genetic Disorders (2017, background): “The majority of diseases in OMIM (74%; 2,527 of 3,397) affected three or fewer tissues out of the 25 tissues included in this study (Figure 1C), illustrating tissue-specific manifestation for genetic disorders (Barshir et al., 2014; Goh et al., 2007).”
Multilayer Analysis and Visualization of Networks (2014, background): “This has led to signiﬁcant improvements in scientiﬁc understanding of numerous phenomena — including genetic and metabolic processes [7–9, 11, 22–26], neuronal connectivity of living beings [27–36], and more.”