ArticleGene therapy2019
Inferring novel genes related to oral cancer with a network embedding method and one-class learning algorithms.
Article in Gene therapy, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed.
- A functional analysis of omic network embedding spaces reveals key altered functions in cancer.Bioinformatics (Oxford, England) · 2023Article
- Deep semi-supervised learning ensemble framework for classifying co-mentions of human proteins and phenotypes.BMC bioinformatics · 2021Article
- DeepGP: An Integrated Deep Learning Method for Endocrine Disease Gene Prediction Using Omics Data.Frontiers in cell and developmental biology · 2021Article
- Investigation of miRNA and mRNA Co-expression Network in Ependymoma.Frontiers in bioengineering and biotechnology · 2020Article
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Authors and funding
6 authors.
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Abstract
Oral cancer (OC) is one of the most common cancers threatening human lives. However, OC pathogenesis has yet to be fully uncovered, and thus designing effective treatments remains difficult. Identifying genes related to OC is an important way for achieving this purpose. In this study, we proposed three computational models for inferring novel OC-related genes. In contrast to previously proposed computational methods, which lacked the learning procedures, each proposed model adopted a one-class learning algorithm, which can provide a deep insight into features of validated OC-related genes. A network embedding algorithm (i.e., node2vec) was applied to the protein-protein interaction network to produce the representation of genes. The features of the OC-related genes were used in the training of the one-class algorithm, and the performance of the final inferring model was improved through a feature selection procedure. Then, candidate genes were produced by applying the trained inferring model to other genes. Three tests were performed to screen out the important candidate genes. Accordingly, we obtained three inferred gene sets, any two of which were different. The inferred genes were also different from previous reported genes and some of them have been included in the public Oral Cancer Gene Database. Finally, we analyzed several inferred genes to confirm whether they are novel OC-related genes.
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