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

By K. Lage, E. Karlberg, Zenia M. Størling, Pall I. Olason, Anders Gorm Pedersen, O. Rigina, A. Hinsby, Z. Tümer, F. Pociot, N. Tommerup and 2 more. Nature Biotechnology, 2007

953 citations recorded by the source corpus.

Subfields: Molecular Biology, Physiology

View Paper: https://www.semanticscholar.org/paper/6480a42afc98c9f62c9fb9f4b8fa938b27e77139

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Citations in the corpus

Computational tools for prioritizing candidate genes: boosting disease gene discovery (2012, background): “The design and development of algorithms that allow computers automatically to learn to recognize complex patterns in data and to make intelligent decisions on the basis of such data. a phenotypically related disease 29 .”
Genome-wide inferring gene-phenotype relationship by walking on the heterogeneous network (2010, background): “Received on December 10, 2009; revised on February 7, 2010; accepted on March 4, 2010”
Visualization of genetic disease-phenotype similarities by multiple maps t-SNE with Laplacian regularization (2014, background): “…computational frameworks for discovering disease-related gene candidates by exploiting complex associations between phenotypes and genotypes found within heterogeneous genomic datasets such as gene expression data, protein-protein interaction networks [5,6] and gene ontology annotations [7].”
A Census of Human Soluble Protein Complexes (2012, neutral): “Consistent with biological expectation (Hartwell et al., 1999; Lage et al., 2007; Oliver, 2000; Vidal et al., 2011), A B”
A Comprehensive Benchmark of Kernel Methods to Extract Protein–Protein Interactions from Literature (2010, background): “Such tools are not only used to populate PPI databases, but their output is often also used directly as independent input to biological data mining (see, e.g., [11,12]).”
The power of protein interaction networks for associating genes with diseases (2010, neutral): “(2008) as one of many integrative features in an support vector machine (SVM) to predict disease annotations, and by Lavallee-Adam et al. (2009) to quantify the distribution of Gene Ontology (Ashburner et al.”
Network-based methods for human disease gene prediction. (2011, methodology): “A couple of text mining techniques were used to map OMIM diseases to different standard vocabularies, Medical Subject Headings (MeSH) or the Unified Medical Language System (UMLS), to score pair-wise similarities among the disease records [49, 50].”
Human Disease-Drug Network Based on Genomic Expression Profiles (2009, background): “Moreover, diseases may be related using statistical analysis of patient records [10], quantitative measurements of the phenotypic overlap of Online Mendelian Inheritance in Man (OMIM) records [11], and annotative concepts [6], and metabolic diseases may be connected via metabolites and common reacti”
Network-Based Elucidation of Human Disease Similarities Reveals Common Functional Modules Enriched for Pluripotent Drug Targets (2010, neutral): “causal genes for diseases [2,3].”
The STRING database in 2011: functional interaction networks of proteins, globally integrated and scored (2010, background): “Indeed, the ‘network view’ on a genome is increasingly being taken in many areas of applied biology: protein networks are used to increase the statistical power in human genetics (1,2), to aid in drug discovery (3,4), to close gaps in metabolic enzyme knowledge (5,6) and to predict phenotypes and ge”
Identifying Relationships among Genomic Disease Regions: Predicting Genes at Pathogenic SNP Associations and Rare Deletions (2009, background): “The general strategy of using function to prioritize genes in disease regions has been substantially explored [11–18].”
Network Medicine: A Network-based Approach to Human Disease (2011, background): “A second set of methods assumes that all cellular components that belong to the same topological, functional or disease module have a high likelihood of being involved in the same diseas”
ChemProt-3.0: a global chemical biology diseases mapping (2016, neutral): “From a biological perspective, we updated our internal human interactome platform to reach 14 421 genes inter-acting through 507 142 unique PPIs (25).”