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RWRMTN: a tool for predicting disease-associated microRNAs based on a microRNA-target gene network
2020 Jun 15 BMC BIOINFORMATICS, 2020
Duc-Hau Le, Trang T.H Tran
Background The misregulation of microRNA (miRNA) has been shown to cause diseases. Recently, we have proposed a computational method based on a random walk framework on a miRNA-target gene network to predict disease-associated miRNAs. The prediction performance of our method is better than that of some existing state-of-the-art network- and machine learning-based methods since it exploits the mutual regulation between miRNAs and their target genes in the miRNA-target gene interaction networks. Results To facilitate the use of this method, we have developed a Cytoscape app, named RWRMTN, to predict disease-associated miRNAs. RWRMTN can work on any miRNA-target gene network. Highly...
Read more articleMachine learning-based approaches for disease gene prediction
2020 Jun 22 BRIEFINGS IN FUNTIONAL GENOMICS, 2020
Duc-Hau Le
Disease gene prediction is an essential issue in biomedical research. In the early days, annotation-based approaches were proposed for this problem. With the development of high-throughput technologies, interaction data between genes/proteins have grown quickly and covered almost genome and proteome; thus, network-based methods for the problem become prominent. In parallel, machine learning techniques, which formulate the problem as a classification, have also been proposed. Here, we firstly show a roadmap of the machine learning-based methods for the disease gene prediction. In the beginning, the problem was usually approached using a binary classification, where positive and negative training sample sets are...
Read more articleDisease subtyping using community detection from consensus networks
2020 Dec 16 KSE 2020
Hung Nguyen, Bang Tran, Duc Tran, Quang-Huy Nguyen, Duc-Hau Le, Tin Nguyen
Disease gene prediction is an essential issue in biomedical research. In the early days, annotation-based approaches were proposed for this problem. With the development of high-throughput technologies, interaction data between genes/proteins have grown quickly and covered almost genome and proteome; thus, network-based methods for the problem become prominent. In parallel, machine learning techniques, which formulate the problem as a classification, have also been proposed. Here, we firstly show a roadmap of the machine learning-based methods for the disease gene prediction. In the beginning, the problem was usually approached using a binary classification, where positive and negative training sample sets are...
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