An integrated approach to inferring gene–disease associations in humans

Radivojac, Predrag; Peng, Kang; Clark, Wyatt T.; Peters, Brandon; Mohan, Amrita; Boyle, Sean M.; Mooney, Sean D.

doi:10.1002/prot.21989

Cited by 169 publications

(132 citation statements)

References 62 publications

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“…In which, the problem is considered as a classification one, where a classifier is learned from training data; then the learned classifier is used to predict whether or not a test/candidate gene is a disease gene. Briefly, at the early, machine learningbased studies usually approached disease gene prediction as a binary classification problem [9], where the learning samples are comprised of positive training samples and negative training samples [9] such as decision trees (DT) [10,11] k-nearest neighbor (kNN) [12], naive Bayesian classifier [13,14], binary support vector machine classifier [15][16][17], artificial neural network (ANN) techniques [18] and random forest (RF) [9]. In these binary classifier-based methods, positive training samples are constructed from known disease genes, whereas negative training samples are the remaining which are not known to be associated with diseases.…”

Section: Introductionmentioning

confidence: 99%

Ontology-based disease similarity network for disease gene prediction

Dang²

2016

Vietnam J Comput Sci

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Finding underlying molecular mechanisms of diseases is one of the important issues in biomedical research. In which, prediction of novel disease-associated genes is mostly focused. Many methods have been proposed based on biological networks and shown effectively for the problem. These network-based methods are usually relied on a "disease module" principle that functionally similar genes are associated with similar phenotypes or diseases. Among them, methods solely based on gene/protein networks only exploit that principle by structural modules in the gene/protein networks. Meanwhile, others based on integration of these networks with a disease similarity network better exploit the principle and consequently result in higher prediction performance. In these studies, the disease similarity network is extracted from a disease similarity matrix which was calculated using text mining techniques on OMIM records. Considering that diseases have been recently well annotated by human phenotype ontology (i.e., a controlled vocabulary database) and semantic similarity measures can be used to calculate similarities among them. Therefore, it would be more accurate to construct disease similarity network based on semantic similarity measures on phenotype ontol- Experiment results show that the ontology-based disease similarity network much improves the prediction performance compared to the one based on OMIM records, irrespective of gene/protein networks. In addition, we show ability of our method in predicting novel Alzheimer's disease-associated genes, in which 19 out of top 100 ranked candidate genes are supported with evidences from literature.

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Section: Introductionmentioning

confidence: 99%

Ontology-based disease similarity network for disease gene prediction

Dang²

2016

Vietnam J Comput Sci

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“…As benchmark data, we used the PPI network data in yeast from Collins et al [20] and also the one in human from Radivojac et al [21]. These networks contain one large connected component and a number of small components.…”

Section: Benchmark Data Setupmentioning

confidence: 99%

Global Network Alignment Method Using Node Similarity Based on Network Characteristics

Afuso

Okazaki

Nakamura

2013

IPSJ Transactions on Bioinformatics

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Various methods to compare given biological networks have been proposed to date. For an instance, is one of such popular methods. However, the method uses only local structural information to calculate a similarity among nodes. Owing to this limitation, the resulted alignment may not reflect the global features of the given networks. In social network analysis certain measurements, so-called network characteristics are used to capture some features of nodes in graphs. And some of these reflect global features of nodes in networks. In this paper, we proposed a network alignment method using a node similarity based on network characteristics so that resulted alignment would reflect the global structural features more than the traditional method. We compared our proposed method with traditional network alignment method, MI-GRAAL, to demonstrate the effectiveness of our proposal. The experiment was carried out through protein-protein interactions (PPI) networks of yeast and human. The results showed that proposed method led to better alignment in view of topological quality than MI-GRAAL.

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“…Protein-protein interaction (PPI) links connect pairs of genes in accordance with combined physical interaction data collected from HPRD, the Online Predicted Human Interaction Database (OPHID), and studies by Rual [7] and Stelzl [8]. Further details about these datasets, which are publicly available, can be found in [9].…”

Section: B Disease-gene Networkmentioning

confidence: 99%

Multi-relational Link Prediction in Heterogeneous Information Networks

Davis

Lichtenwalter

Chawla

2011

2011 International Conference on Advances in Social Networks Analysis and Mining

120

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Abstract-Many important real-world systems, modeled naturally as complex networks, have heterogeneous interactions and complicated dependency structures. Link prediction in such networks must model the influences between heterogenous relationships and distinguish the formation mechanisms of each link type, a task which is beyond the simple topological features commonly used to score potential links. In this paper, we introduce a novel probabilistically weighted extension of the Adamic/Adar measure for heterogenous information networks, which we use to demonstrate the potential benefits of diverse evidence, particularly in cases where homogeneous relationships are very sparse. However, we also expose some fundamental flaws of traditional a priori link prediction. In accordance with previous research on homogeneous networks, we further demonstrate that a supervised approach to link prediction can enhance performance and is easily extended to the heterogeneous case. Finally, we present results on three diverse, real-world heterogeneous information networks and discuss the trends and tradeoffs of supervised and unsupervised link prediction in a multi-relational setting.

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An integrated approach to inferring gene–disease associations in humans

Cited by 169 publications

References 62 publications

Ontology-based disease similarity network for disease gene prediction

Ontology-based disease similarity network for disease gene prediction

Global Network Alignment Method Using Node Similarity Based on Network Characteristics

Multi-relational Link Prediction in Heterogeneous Information Networks

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