scholarly journals Towards Prediction and Prioritization of disease genes by the modularity of human phenome-genome assembled network

2010 ◽  
Vol 7 (2) ◽  
Author(s):  
Jeffrey Q. Jiang ◽  
Andreas W. M. Dress ◽  
Ming Chen
Keyword(s):  
Genetic Diseases ◽  
Interaction Network ◽  
Modular Organization ◽  
Disease Genes ◽  
Human Interactome ◽  
Protein Protein Interaction ◽  
Gene Associations ◽  
Genetic Overlap ◽  
Genetic Landscape ◽  
Leave One Out

SummaryEmpirical clinical studies on the human interactome and phenome not only illustrates prevalent phenotypic overlap and genetic overlap between diseases, but also reveals a modular organization of the genetic landscape of human disease, provding new opportunities to reduce the complexity in dissecting the phenotype-genotype association. We here introduce a network-module based method towards phenotype-genotype association inference and disease gene identification. This approach incorporates protein-protein interaction network, phenotype similarity network and known phenotype-genotype associations into an assembled network. We then decomposes the resulted network into modules (or communities) wherein we identified and prioritized the disease genes from the candidates within the loci associated with the query disease using a linear regression model and concordance score. For the known phenotype-gene associations in the OMIM database, we used the leave-one-out validation to evaluate the feasibility of our method, and successfully ranked known disease genes at top 1 in 887 out of 1807 cases. Moreover, applying this approach on 850 OMIMloci characterized by an unknown molecular basis, we propose high-probability candidates for 81 genetic diseases.

scholarly journals Gene co-expression in the interactome: moving from correlation toward causation via an integrated approach to disease module discovery

2021 ◽  
Vol 7 (1) ◽  
Author(s):  
Paola Paci ◽  
Giulia Fiscon ◽  
Federica Conte ◽  
Rui-Sheng Wang ◽  
Lorenzo Farina ◽  
...  
Keyword(s):  
Network Analysis ◽  
Interaction Network ◽  
Integrated Approach ◽  
State Transitions ◽  
Disease Genes ◽  
Human Interactome ◽  
Protein Protein Interaction ◽  
Interactome Network ◽  
Module Discovery ◽  
Specific Disorders

AbstractIn this study, we integrate the outcomes of co-expression network analysis with the human interactome network to predict novel putative disease genes and modules. We first apply the SWItch Miner (SWIM) methodology, which predicts important (switch) genes within the co-expression network that regulate disease state transitions, then map them to the human protein–protein interaction network (PPI, or interactome) to predict novel disease–disease relationships (i.e., a SWIM-informed diseasome). Although the relevance of switch genes to an observed phenotype has been recently assessed, their performance at the system or network level constitutes a new, potentially fascinating territory yet to be explored. Quantifying the interplay between switch genes and human diseases in the interactome network, we found that switch genes associated with specific disorders are closer to each other than to other nodes in the network, and tend to form localized connected subnetworks. These subnetworks overlap between similar diseases and are situated in different neighborhoods for pathologically distinct phenotypes, consistent with the well-known topological proximity property of disease genes. These findings allow us to demonstrate how SWIM-based correlation network analysis can serve as a useful tool for efficient screening of potentially new disease gene associations. When integrated with an interactome-based network analysis, it not only identifies novel candidate disease genes, but also may offer testable hypotheses by which to elucidate the molecular underpinnings of human disease and reveal commonalities between seemingly unrelated diseases.

scholarly journals Genome-wide inferring gene–phenotype relationship by walking on the heterogeneous network

2010 ◽  
Vol 26 (9) ◽  
pp. 1219-1224 ◽  
Author(s):  
Yongjin Li ◽  
Jagdish C. Patra
Keyword(s):  
Heterogeneous Network ◽  
Gene Network ◽  
Genetic Diseases ◽  
Supplementary Information ◽  
Disease Genes ◽  
Phenotypic Data ◽  
Disease Associations ◽  
Improved Performance ◽  
Leave One Out ◽  
Phenotype Network

Abstract Motivation: Clinical diseases are characterized by distinct phenotypes. To identify disease genes is to elucidate the gene–phenotype relationships. Mutations in functionally related genes may result in similar phenotypes. It is reasonable to predict disease-causing genes by integrating phenotypic data and genomic data. Some genetic diseases are genetically or phenotypically similar. They may share the common pathogenetic mechanisms. Identifying the relationship between diseases will facilitate better understanding of the pathogenetic mechanism of diseases. Results: In this article, we constructed a heterogeneous network by connecting the gene network and phenotype network using the phenotype–gene relationship information from the OMIM database. We extended the random walk with restart algorithm to the heterogeneous network. The algorithm prioritizes the genes and phenotypes simultaneously. We use leave-one-out cross-validation to evaluate the ability of finding the gene–phenotype relationship. Results showed improved performance than previous works. We also used the algorithm to disclose hidden disease associations that cannot be found by gene network or phenotype network alone. We identified 18 hidden disease associations, most of which were supported by literature evidence. Availability: The MATLAB code of the program is available at http://www3.ntu.edu.sg/home/aspatra/research/Yongjin_BI2010.zip Contact: [email protected] Supplementary information: Supplementary data are available at Bioinformatics online.

IDENTIFICATION OF NOVEL GENES RELATED TO DIABETIC RETINOPATHY USING PROTEIN–PROTEIN INTERACTION NETWORK AND GENE ONTOLOGIES

2016 ◽  
Vol 24 (01) ◽  
pp. 117-127 ◽  
Author(s):  
S. UMADEVI ◽  
K. PREMKUMAR ◽  
S. VALARMATHI ◽  
P. M. AYYASAMY ◽  
S. RAJAKUMAR
Keyword(s):  
Diabetic Retinopathy ◽  
Protein Interaction ◽  
Protein Interaction Network ◽  
Interaction Network ◽  
Disease Genes ◽  
Protein Kinase Activity ◽  
Peptidase Activity ◽  
Novel Genes ◽  
Protein Protein Interaction ◽  
Protein Protein Interaction Network

Diabetic retinopathy is the most common cause of blindness, associated with many biochemical pathways mediated by several genes and proteins. Disease gene identification can be achieved through several approaches but still it is a challenging task. This study, aimed to find out the novel genes associated with diabetic retinopathy. In this study, all the well-known genes associated with diabetic retinopathy were collected from databases and the protein interaction partners were identified. The interacting candidate genes were chosen by chromosomal locations, sharing with disease genes. The protein–protein interaction network was constructed and the key nodes (genes) were identified by degree, betweenness centrality, closeness centrality and eccentricity centrality. Further, the ontological terms, molecular function, biological process and cellular components were related with that of the disease genes with p-value [Formula: see text]. The genes UBC, FOS, ITGB1, FOXA2, CCND1, FOSL1, RXRA and NCAM1 were identified as potential genes associated with diabetic retinopathy. The molecular functions of these genes include protein binding, receptor activity, receptor binding, oxidoreductase activity, protein kinase activity, serine-type peptidase activity and growth factor. Many of the identified genes were clinically related as evidence by the literature.

scholarly journals Dynamic rewiring of the human interactome by interferon signalling

2019 ◽  
Author(s):  
Craig H. Kerr ◽  
Michael A. Skinnider ◽  
Angel M. Madero ◽  
Daniel D.T. Andrews ◽  
R. Greg Stacey ◽  
...  
Keyword(s):  
Protein Interactions ◽  
Interaction Network ◽  
Cell Types ◽  
Antiviral Response ◽  
Type I ◽  
Protein Protein Interactions ◽  
Human Interactome ◽  
Viral Pathogens ◽  
Protein Protein Interaction ◽  
Dependent Protein

ABSTRACTBackgroundThe type I interferon (IFN) response is an ancient pathway that protects cells against viral pathogens by inducing the transcription of hundreds of IFN-stimulated genes (ISGs). Transcriptomic and biochemical approaches have established comprehensive catalogues of ISGs across species and cell types, but their antiviral mechanisms remain incompletely characterized. Here, we apply a combination of quantitative proteomic approaches to delineate the effects of IFN signalling on the human proteome, culminating in the use of protein correlation profiling to map IFN-induced rearrangements in the human protein-protein interaction network.ResultsWe identified >27,000 protein interactions in IFN-stimulated and unstimulated cells, many of which involve proteins associated with human disease and are observed exclusively within the IFN-stimulated network. Differential network analysis reveals interaction rewiring across a surprisingly broad spectrum of cellular pathways in the antiviral response. We identify IFN-dependent protein-protein interactions mediating novel regulatory mechanisms at the transcriptional and translational levels, with one such interaction modulating the transcriptional activity of STAT1. Moreover, we reveal IFN-dependent changes in ribosomal composition that act to buffer ISG protein synthesis.ConclusionsOur map of the IFN interactome provides a global view of the complex cellular networks activated during the antiviral response, placing ISGs in a functional context, and serves as a framework to understand how these networks are dysregulated in autoimmune or inflammatory disease.

Enhancing the prediction of disease–gene associations with multimodal deep learning

2019 ◽  
Vol 35 (19) ◽  
pp. 3735-3742 ◽  
Author(s):  
Ping Luo ◽  
Yuanyuan Li ◽  
Li-Ping Tian ◽  
Fang-Xiang Wu
Keyword(s):  
Disease Gene ◽  
Computational Prediction ◽  
Integrative Model ◽  
Supplementary Information ◽  
Disease Genes ◽  
Protein Protein Interaction ◽  
Gene Associations ◽  
Deep Belief Net ◽  
Latent Representations ◽  
Disease Associated Genes

Abstract Motivation Computationally predicting disease genes helps scientists optimize the in-depth experimental validation and accelerates the identification of real disease-associated genes. Modern high-throughput technologies have generated a vast amount of omics data, and integrating them is expected to improve the accuracy of computational prediction. As an integrative model, multimodal deep belief net (DBN) can capture cross-modality features from heterogeneous datasets to model a complex system. Studies have shown its power in image classification and tumor subtype prediction. However, multimodal DBN has not been used in predicting disease–gene associations. Results In this study, we propose a method to predict disease–gene associations by multimodal DBN (dgMDL). Specifically, latent representations of protein-protein interaction networks and gene ontology terms are first learned by two DBNs independently. Then, a joint DBN is used to learn cross-modality representations from the two sub-models by taking the concatenation of their obtained latent representations as the multimodal input. Finally, disease–gene associations are predicted with the learned cross-modality representations. The proposed method is compared with two state-of-the-art algorithms in terms of 5-fold cross-validation on a set of curated disease–gene associations. dgMDL achieves an AUC of 0.969 which is superior to the competing algorithms. Further analysis of the top-10 unknown disease–gene pairs also demonstrates the ability of dgMDL in predicting new disease–gene associations. Availability and implementation Prediction results and a reference implementation of dgMDL in Python is available on https://github.com/luoping1004/dgMDL. Supplementary information Supplementary data are available at Bioinformatics online.

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