Analysis of protein sequence and interaction data for candidate disease gene prediction

George, Richard A.; Liu, Jason Y.; Feng, Lina L.; Bryson-Richardson, Robert J.; Fatkin, Diane; Wouters, Merridee A.

doi:10.1093/nar/gkl707

Cited by 145 publications

(115 citation statements)

References 48 publications

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“…For this reason, any reference to interactions between genes in this paper refers to the interactions between their products. Existing methods can be classified into two main categories; (i) localized methods, i.e., methods based on direct interactions and shortest paths between known disease genes and candidate genes [3,7,13], (ii) global methods, i.e., methods that model the information flow in the cell to assess the proximity and connectivity between known disease genes and candidate genes. Several studies show that global approaches, such as random walk and network propagation, clearly outperform local approaches [10,11].…”

Section: Background and Motivationmentioning

confidence: 99%

Role of Centrality in Network-Based Prioritization of Disease Genes

Erten

Koyutürk

2010

Evolutionary Computation, Machine Learning and Data Mining in Bioinformatics

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Abstract. High-throughput molecular interaction data have been used effectively to prioritize candidate genes that are linked to a disease, based on the notion that the products of genes associated with similar diseases are likely to interact with each other heavily in a network of protein-protein interactions (PPIs). An important challenge for these applications, however, is the incomplete and noisy nature of PPI data. Random walk and network propagation based methods alleviate these problems to a certain extent, by considering indirect interactions and multiplicity of paths. However, as we demonstrate in this paper, such methods are likely to favor highly connected genes, making prioritization sensitive to the skewed degree distribution of PPI networks, as well as ascertainment bias in available interaction and disease association data. Here, we propose several statistical correction schemes that aim to account for the degree distribution of known disease and candidate genes. We show that, while the proposed schemes are very effective in detecting loosely connected disease genes that are missed by existing approaches, this improvement might come at the price of more false negatives for highly connected genes. Motivated by these results, we develop uniform prioritization methods that effectively integrate existing methods with the proposed statistical correction schemes. Comprehensive experimental results on the Online Mendelian Inheritance in Man (OMIM) database show that the resulting hybrid schemes outperform existing methods in prioritizing candidate disease genes.

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Section: Background and Motivationmentioning

confidence: 99%

Role of Centrality in Network-Based Prioritization of Disease Genes

Erten

Koyutürk

2010

Evolutionary Computation, Machine Learning and Data Mining in Bioinformatics

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“…For example, POCUS (Turner et al 2003) searches candidate genes by identifying an enrichment of keywords associated with GO, shared InterPro domains (Mulder et al 2005) and expression profiles among a given set of susceptibility loci relative to the genome at large. Moreover, the Common Pathway Scanning (CPS) applies network data derived from protein-protein interaction (PPI) and pathway databases to identify relationships between genes as well as the Common Module Profiling (CMP) identifies likely candidates using a domain-dependent sequence similarity approach, based on the hypothesis that disruption of genes of similar function will lead to the same phenotype (George et al 2006).…”

Section: Introductionmentioning

confidence: 99%

Virus proteins similar to human proteins as possible disturbance on human pathways

2014

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Cancer is not rare anywhere in the world now, and the global burden of cancer continues to increase largely every year. Previous research on infections and cancers reported that, about 17.8 % of the cancers worldwide, which are over 1.9 million cases of cancer, are related to viral infections. At least six oncoviruses, cancer-causing viruses, have been known so far, which include hepatitis B virus, hepatitis C virus, Epstein-Barr virus (EBV or HHV-4), human papillomavirus, human T lymphotropic virus type 1, Kaposi's sarcoma-associated herpesvirus (KSHV or HHV-8), but the pathogenic mechanism is far from being completely understood. In this study, assuming that finding human proteins significantly similar to viral oncoproteins leads to a categorization of the cancer-related pathways that are currently not clearly known, we analyzed different types of virus-caused cancers based on their similarity in order to clarify the unknown cancer mechanisms. As a result, we obtained several potential tumor pathways that may be significant and essential in oncogenic cancer process, which will be helpful for further study on cancer mechanisms and the development of new drug targets.

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“…Several techniques uncovers gene-disease associations taking an integrative approach, leveraging Gene Ontology annotations [1][2][3][4][5][6], genes expression [7][8],protein sequences [9], biological pathways [4], Bio-text mining [10][11], and transcription factor binding sites [4] and several phenotypic traits of diseases.…”

Section: Introductionmentioning

confidence: 99%

Protein Network for Associating Genes with Dementia

Gupta¹,

Mishra²

2013

IJCA

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Association between causal genes and their genetic diseases is an important problem concerning human health. Linkage analysis is such a method that can identify which unknown disease genes are located in chromosomal region out of hundreds of candidate genes according to their functions, interactions, and pathways which is good identification of genes associated with general/hereditary disorders.Here, we used method for prioritization of candidate genes of Dementia by the use of a global network distance measure, Random Walk Analysis, which detects neurological disorder been associated with distribution of sub-network among the genes.

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Analysis of protein sequence and interaction data for candidate disease gene prediction

Cited by 145 publications

References 48 publications

Role of Centrality in Network-Based Prioritization of Disease Genes

Role of Centrality in Network-Based Prioritization of Disease Genes

Virus proteins similar to human proteins as possible disturbance on human pathways

Protein Network for Associating Genes with Dementia

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