School of Mathematical Sciences, Shanghai Jiao Tong University, Shanghai, China.
Institute of Interdisciplinary Integrative Medicine Research, Shanghai University of Traditional Chinese Medicine, Shanghai, China.
Comput Biol Chem. 2020 Dec;89:107406. doi: 10.1016/j.compbiolchem.2020.107406. Epub 2020 Oct 20.
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.
miRNAs 和长非编码 RNA（lncRNAs）之间的相互作用是目前研究的热点，因为它们在基因调控中起着关键作用。由于实验方法鉴定潜在相互作用的成本较高，因此计算预测 lncRNA-miRNA 相互作用已成为一种很受欢迎的替代策略。基于实验验证的 lncRNA 和 miRNA 之间的相互作用，开发基于机器学习的 lncRNA-miRNA 预测模型是很有必要的。基于机器学习技术的现有模型的准确性和稳健性有待进一步提高。考虑到 lncRNA 和 miRNA 的属性在这两种 RNA 之间的相互作用中起着重要作用，我们提出了一种名为 LMI-DForest 的深度学习模型，该模型结合了深度森林和自动编码器策略。在实验验证的 lncRNA-miRNA 相互作用数据集上进行的系统比较表明，该方法在 lncRNA-miRNA 相互作用预测中始终优于其他机器学习模型。
