LMI-DForest: A deep forest model towards the prediction of lncRNA-miRNA interactions. (December 2020)

• Record Type: Journal Article
• Authors: Wang, Wei; Guan, Xiaoqing; Khan, Muhammad Tahir; Xiong, Yi; Wei, Dong-Qing
• Is Part Of: Computational biology and chemistry. Volume 89(2020)
• Publication Date: 2020-12
• Subjects: Deep learning -- DeepForest -- lncRNAs -- miRNAs -- lncRNA-miRNA interaction; Chemistry -- Data processing -- Periodicals; Biology -- Data processing -- Periodicals; Biochemistry -- Data processing; Biology -- Data processing; Molecular biology -- Data processing; Periodicals; Electronic journals; 542.85
• DOI: 10.1016/j.compbiolchem.2020.107406
• Languages: English
• ISSNs: 1476-9271

Abstract

Graphical abstract: Highlights: Based on DeepForest, LMI-DForest is proposed here by combining the deep forest and autoencoder strategies. The proposed LMI-DForest is applied to the classification of constructed dataset of lncRNA-miRNA interactions. LMI-DForest shows superior performance over the other machine learning models in the prediction of lncRNA-miRNA interactions. Abstract: The interactions between miRNAs and long non-coding RNAs (lncRNAs) are subject to intensive recent studies due to its critical role in gene regulations. Computational prediction of lncRNA-miRNA interactions has become a popular alternative strategy to the experimental methods for identification of underlying interactions. It is desirable to develop the machine learning-based models for prediction of lncRNA-miRNA based on the experimentally validated interactions between lncRNAs and miRNAs. The accuracy and robustness of existing models based on machine learning techniques are subject to further improvement. Considering that the attributes of lncRNA and miRNA contribute key importance in the interaction between these two RNAs, a deep learning model, named LMI-DForest, is proposed here by combining the deep forest and autoencoder strategies. Systematic comparison on the experiment validated datasets for lncRNA-miRNA interaction datasets demonstrates that the proposed method consistently shows superior performance over the other machine learning models in the lncRNA-miRNA interaction prediction.