Open AccessAdopting a new sample strategy to predict miRNA-disease associations
Author(s) -
Xing Zhong,
Lei Tian,
RongHui Rong
Publication year - 2021
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1757/1/012110
Subject(s) - support vector machine , disease , microrna , adaboost , machine learning , artificial intelligence , sample (material) , cross validation , computer science , computational biology , bioinformatics , oncology , medicine , biology , gene , genetics , chemistry , chromatography
Exploring unknown miRNA-disease associations by computational tools is a new way to study the correlations between genes and diseases. in this paper, we proposed a new model named ABPUSVM, which consisted of a new sample strategy based on Positive-Unlabeled learning and a prediction model that combined AdaBoost and SVM. When ABPUSVM was applied to predict unknown associations of miRNA-disease, the AUC of 0.9383 improved greatly based on 5 folds cross-validation and showed its excellent performance, which indicated ABPUSVM was significantly better than other classic models. Afterward, a case study confirmed that 46 out of the top predicted 50 miRNAs of breast cancer by ABPUSVM were supported by three databases. whose results showed the dataset by ABPUSVM were significantly better than that of the other methods. All results have shown that ABPUSVM is a promising and potential tool for exploring the associations of miRNA-disease.