Gene prioritization using a probabilistic knowledge model

Wang, Shuguang; Hauskrecht, Miloš; Visweswaran, Shyam

doi:10.1109/bibmw.2009.5332107

Cited by 2 publications

(1 citation statement)

References 13 publications

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“…First, they built the network without removing the insignificant genes from the network, while the second one was based on the Diffusion Rank (DR) algorithm (Yang et al, 2007 ). The NDRC simulates the heat diffusion process where information flows from the known disease genes of related disease and propagates over the PPI network with noisy data as a problem to prioritize disease genes (Li et al, 2009 ; Wang et al, 2009 ; Fang et al, 2014 ). Multiple kernels learning (KML) and N dimensional order statistic (NDOS) methods were found to be handling noisy data effectively.…”

Section: Ppi Network and Inherited Diseasesmentioning

confidence: 99%

Data Mining and Pattern Recognition Models for Identifying Inherited Diseases: Challenges and Implications

et al. 2016

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Data mining and pattern recognition methods reveal interesting findings in genetic studies, especially on how the genetic makeup is associated with inherited diseases. Although researchers have proposed various data mining models for biomedical approaches, there remains a challenge in accurately prioritizing the single nucleotide polymorphisms (SNP) associated with the disease. In this commentary, we review the state-of-art data mining and pattern recognition models for identifying inherited diseases and deliberate the need of binary classification- and scoring-based prioritization methods in determining causal variants. While we discuss the pros and cons associated with these methods known, we argue that the gene prioritization methods and the protein interaction (PPI) methods in conjunction with the K nearest neighbors' could be used in accurately categorizing the genetic factors in disease causation.

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Section: Ppi Network and Inherited Diseasesmentioning

confidence: 99%

Data Mining and Pattern Recognition Models for Identifying Inherited Diseases: Challenges and Implications

et al. 2016

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Computational Disease Gene Prioritization: An Appraisal

Gill

Singh

Aseri

2014

Journal of Computational Biology

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Bioinformatics aids in the understanding of the biological processes of living beings and the genetic architecture of human diseases. The discovery of disease-related genes improves the diagnosis and therapy design for the disease. To save the cost and time involved in the experimental verification of the candidate genes, computational methods are employed for ranking the genes according to their likelihood of being associated with the disease. Only top-ranked genes are then verified experimentally. A variety of methods have been conceived by the researchers for the prioritization of the disease candidate genes, which differ in the data source being used or the scoring function used for ranking the genes. A review of various aspects of computational disease gene prioritization and its research issues is presented in this article. The aspects covered are gene prioritization process, data sources used, types of prioritization methods, and performance assessment methods. This article provides a brief overview and acts as a quick guide for disease gene prioritization.

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Gene prioritization using a probabilistic knowledge model

Cited by 2 publications

References 13 publications

Data Mining and Pattern Recognition Models for Identifying Inherited Diseases: Challenges and Implications

Data Mining and Pattern Recognition Models for Identifying Inherited Diseases: Challenges and Implications

Computational Disease Gene Prioritization: An Appraisal

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