Network-based ranking methods for prediction of novel disease associated microRNAs

Le, Duc-Hau

doi:10.1016/j.compbiolchem.2015.07.003

Cited by 42 publications

(22 citation statements)

References 63 publications

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“…In recent decades, the prediction and ranking of disease-related miRNAs have received much attention16171819. Experimental verification method for the identification of disease-associated miRNAs is expensive and time-consuming2021.…”

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confidence: 99%

Network Consistency Projection for Human miRNA-Disease Associations Inference

Liao

et al. 2016

Sci Rep

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Prediction and confirmation of the presence of disease-related miRNAs is beneficial to understand disease mechanisms at the miRNA level. However, the use of experimental verification to identify disease-related miRNAs is expensive and time-consuming. Effective computational approaches used to predict miRNA-disease associations are highly specific. In this study, we develop the Network Consistency Projection for miRNA-Disease Associations (NCPMDA) method to reveal the potential associations between miRNAs and diseases. NCPMDA is a non-parametric universal network-based method that can simultaneously predict miRNA-disease associations in all diseases but does not require negative samples. NCPMDA can also confirm the presence of miRNAs in isolated diseases (diseases without any known miRNA association). Leave-one-out cross validation and case studies have shown that the predictive performance of NCPMDA is superior over that of previous method.

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confidence: 99%

Network Consistency Projection for Human miRNA-Disease Associations Inference

Liao

et al. 2016

Sci Rep

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“…Because this algorithm calculates a global similarity among candidate and known disease genes on whole network and therefore not only genes directly connected to disease genes are considered, but also indirect ones. This algorithm has been successfully applied to other problems such as prediction of disease-associated miRNAs [27] and protein complexes [28]. However, this method can only exploit the "disease module" in the gene/protein network (i.e., genes/proteins associated with the same or similar diseases usually form functional/physical modules on gene/protein interaction networks [29][30][31]).…”

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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“…Trong số các phƣơng pháp phân hạng gen dựa trên mạng, phƣơng pháp sử dụng thuật toán bƣớc ngẫu nhiên có quay lại RWR [10][11][12] đƣợc áp dụng ph biến hơn các phƣơng pháp khác vì thuật toán này xem xét toàn bộ các liên kết giữa các gen gây bệnh đã biết với các gen ứng viên trên mạng tƣơng tác gen/protein, bao gồm cả các tƣơng tác trực tiếp và gián tiếp. Không những đạt đƣợc hiệu quả cao trong bài toán phân hạng gen ứng viên liên quan đến bệnh, thuật toán này còn đƣợc sử dụng hiệu quả trong việc xác định các microRNA mới liên quan đến bệnh [13] cũng nhƣ các đích tác động mới của thuốc [14]. Tiếp nối thành công của thuật toán RWR cho bài toán phân hạng và tìm kiếm gen gây bệnh trên mạng tƣơng tác gen/protein đồng nhất.…”

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