LMI-DForest: A deep forest model towards the prediction of lncRNA-miRNA interactions |
| |
| Institution: | 1. School of Mathematical Sciences, Shanghai Jiao Tong University, Shanghai, China;2. Institute of Interdisciplinary Integrative Medicine Research, Shanghai University of Traditional Chinese Medicine, Shanghai, China;3. Institute of Molecular Biology and Biotechnology, The University of Lahore Pakistan, Pakistan;4. State Key Laboratory of Microbial Metabolism, Joint International Research Laboratory of Metabolic and Developmental Sciences, School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai, China;5. Peng Cheng Laboratory, Shenzhen, Guangdong, China;1. College of Computer Science and Electronic Engineering, Hunan University, Changsha, Hunan 410082, China;2. College of Computer Engineering & Applied Mathematics, Changsha University, Changsha, Hunan 410003, China;1. School of Computer, Electronic and Information, Guangxi University, Nanning, China;2. Hunan Provincial Key Lab on Bioinformatics, School of Computer Science and Engineering, Central South University, Changsha, China;3. The Network Center, Kunming University of Science and Technology, Kunming, China;1. School of Computer Science and Engineering, Central South University, Changsha 410083, China;2. Division of Biomedical Engineering and Department of Mechanical Engineering, University of Saskatchewan, Saskatoon, SK S7N5A9, Canada;3. Department of Computer Science, Old Dominion University, VA 23507 Norfolk, USA;1. School of Information and Control Engineering, China University of Mining and Technology, Xuzhou 221116, China;2. Xinjiang Technical Institute of Physics and Chemistry, Chinese Academy of Science, Ürümqi 830011, China;1. School of Computer Science and Technology, Beijing Institute of Technology, Beijing, 100081, China;2. Institute of Fundamental and Frontier Sciences, University of Electronic Science and Technology of China, Chengdu, China;3. Genetics Department, Faculty of Agriculture, Beni-Suef University, Beni-Suef, 62511, Egypt;4. Advanced Research Institute of Multidisciplinary Science, Beijing Institute of Technology, Beijing, China |
| |
| 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. |
| |
| Keywords: | Deep learning DeepForest lncRNAs miRNAs lncRNA-miRNA interaction |
| 本文献已被 ScienceDirect 等数据库收录！ |
|
