Network-based global inference of human disease genes

By Xuebing Wu, Rui Jiang, Michael Q. Zhang, Shao Li. Molecular Systems Biology, 2008

675 citations recorded by the source corpus.

Deciphering the genetic basis of human diseases is an important goal of biomedical research. On the basis of the assumption that phenotypically similar diseases are caused by functionally related genes, we propose a computational framework that integrates human protein–protein interactions, disease phenotype similarities, and known gene–phenotype associations to capture the complex relationships between phenotypes and genotypes. We develop a tool named CIPHER to predict and prioritize disease genes, and we show that the global concordance between the human protein network and the phenotype network reliably predicts disease genes. Our method is applicable to genetically uncharacterized phenotypes, effective in the genome‐wide scan of disease genes, and also extendable to explore gene cooperativity in complex diseases. The predicted genetic landscape of over 1000 human phenotypes, which reveals the global modular organization of phenotype–genotype relationships. The genome‐wide prioritization of candidate genes for over 5000 human phenotypes, including those with under‐characterized disease loci or even those lacking known association, is publicly released to facilitate future discovery of disease genes.

Subfields: Molecular Biology

View Paper: https://www.semanticscholar.org/paper/3eb58a9b1f7796f383edd423ada256cd74043edb

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Citations in the corpus

Associating Genes and Protein Complexes with Disease via Network Propagation (2010, methodology): “The Cipher method [9] is based on computing protein closeness in a PPI network.”
Network-based methods for human disease gene prediction. (2011, methodology): “[55] used a simple linear regression method called CIPHER (Correlating protein Interaction network and PHEnotype network to pRedict disease genes) to model the correlation between phenotype similarity profile and gene closeness profile in the PPI network.”
Systems medicine: the future of medical genomics and healthcare (2009, background): “put technologies, computational tools and integrated knowledge bases, it has become possible to establish new links between genes, biological functions and a wide range of human diseases [148-153].”
ProDiGe: Prioritization Of Disease Genes with multitask machine learning from positive and unlabeled examples (2011, background): “, by matching the functional annotations of candidate genes to the disease or phenotype under investigation [2-4], many successful approaches assume that some disease genes are already known and try to detect candidate genes which share similarities with known disease genes for the phenotype under i”
Disease candidate gene identification and prioritization using protein interaction networks (2009, methodology): “Although PPIs have been used widely to identify novel disease candidate genes [14,15,23,26-35], besides Kohler et al. [30] and Wu et al. [34], there have been no reports on using PPIs for disease gene prioritization.”
Inductive matrix completion for predicting gene–disease associations (2014, background): “Recently, predicting gene–disease links based on network analysis has become popular (Lee et al., 2011; Li and Patra, 2010; Linghu et al., 2009; Singh-Blom et al., 2013; Vanunu et al., 2010; Wu et al., 2008).”
Enhancing the Prioritization of Disease-Causing Genes through Tissue Specific Protein Interaction Networks (2012, methodology): “These methods are often based on system-wide data such as protein interaction networks [4–9], gene expression [8,10–12], sequence similarity of genes [13,14], functional similarity and annotation [8,12,13] and more (for a review on these methods see [15,16]).”
Network pharmacology: towards the artificial intelligence-based precision traditional Chinese medicine (2023, methodology): “Of note, these above methods have been integrated into the UNIQ system ( U sing N etwork target for I ntelligent and Q uantitative analysis on drug actions, UNIQ), the AI-based R&D platform our group has established to develop various new drugs [14, 15, 16, 17].”
Prediction of disease genes using tissue-specified gene-gene network (2014, positive): “Analyses of recent research studies, according to CIPHER [16], GeneWalker [17], PRINCE [18] and RWRH [19] highlighted the associations that were derived directly from protein interactions to more distant connections in various ways.”
Mining breast cancer genes with a network based noise-tolerant approach (2013, background): “…physical attribute-based methods include screening direct neighbors of known disease genes in the PPI network [7,8], comparing shortest path length [9] between candidate genes and known disease genes, clustering or graph partitioning to uncover disease modules in the interaction network [10-12].”