A human phenome-interactome network of protein complexes implicated in genetic disorders

@article{Lage2007AHP,
  title={A human phenome-interactome network of protein complexes implicated in genetic disorders},
  author={Kasper Lage and E. Olof Karlberg and Zenia Marian St{\o}rling and Pall Isolfur Olason and Anders Gorm Pedersen and Olga Rigina and Anders M{\o}rkeberg Hinsby and Zeynep T{\"u}mer and Flemming Pociot and Niels Tommerup and Yves Moreau and S{\o}ren Brunak},
  journal={Nature Biotechnology},
  year={2007},
  volume={25},
  pages={309-316}
}
We performed a systematic, large-scale analysis of human protein complexes comprising gene products implicated in many different categories of human disease to create a phenome-interactome network. This was done by integrating quality-controlled interactions of human proteins with a validated, computationally derived phenotype similarity score, permitting identification of previously unknown complexes likely to be associated with disease. Using a phenomic ranking of protein complexes linked to… Expand
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References

SHOWING 1-10 OF 60 REFERENCES
Reconstruction of a functional human gene network, with an application for prioritizing positional candidate genes.
TLDR
A functional human gene network is developed that integrates information on genes and the functional relationships between genes, based on data from the Kyoto Encyclopedia of Genes and Genomes, which suggests that this method can significantly reduce the cost and effort of pinpointing true disease genes in analyses of disorders for which numerous loci have been reported but for which most of the genes are unknown. Expand
Predicting disease genes using protein–protein interactions
TLDR
Exploiting protein–protein interactions can greatly increase the likelihood of finding positional candidate disease genes, and when applied on a large scale they can lead to novel candidate gene predictions. Expand
A Human Protein-Protein Interaction Network: A Resource for Annotating the Proteome
TLDR
A large, highly connected network of interacting pairs of human proteins was identified, characterizing ANP32A and CRMP1 as modulators of Wnt signaling and two novel Axin-1 interactions were validated experimentally. Expand
A Protein–Protein Interaction Network for Human Inherited Ataxias and Disorders of Purkinje Cell Degeneration
TLDR
An interaction network for 54 proteins involved in 23 inherited ataxias is developed and expanded by incorporating literature-curated and evolutionarily conserved interactions and provides a tool for understanding pathogenic mechanisms common for this class of neurodegenerative disorders. Expand
A text-mining analysis of the human phenome
TLDR
It is found that similarity between phenotypes reflects biological modules of interacting functionally related genes, including relatedness at the level of protein sequence, protein motifs, functional annotation, and direct protein–protein interaction. Expand
Towards a proteome-scale map of the human protein–protein interaction network
TLDR
An initial version of a proteome-scale map of human binary protein–protein interactions is described, which increases by ∼70% the set of available binary interactions within the tested space and reveals more than 300 new connections to over 100 disease-associated proteins. Expand
Association of genes to genetically inherited diseases using data mining
TLDR
A scoring system for the possible functional relationships of human genes to 455 genetically inherited diseases that have been mapped to chromosomal regions without assignment of a particular gene indicates that for some diseases, the chance of identifying the underlying gene is higher. Expand
G2D: a tool for mining genes associated with disease
TLDR
An algorithm to prioritize genes on a chromosomal region according to their possible relation to an inherited disease using a combination of data mining on biomedical databases and gene sequence analysis is developed. Expand
Creation and implications of a phenome-genome network
TLDR
A network of relations between phenotypic, disease, environmental and experimental contexts as well as genes with differential expression associated with these concepts are found and novel genes related to concepts such as aging are identified. Expand
Gene prioritization through genomic data fusion
TLDR
A bioinformatics approach, together with a freely accessible, interactive and flexible software termed Endeavour, to prioritize candidate genes underlying biological processes or diseases, based on their similarity to known genes involved in these phenomena, offers an alternative integrative method for gene discovery. Expand
...
1
2
3
4
5
...