Shandong University, China and the MSc degree (distinction degree) from Imperial College London, UK, in 2017 and 2018, respectively. She is currently a PhD candidate in Nanyang Technological University, Singapore.
School of Mathematical Sciences from the Dalian University of Technology, in 2007. She is an associate professor with the School of Science, Dalian Maritime University, China.
Brief Bioinform. 2021 Jul 20;22(4). doi: 10.1093/bib/bbaa228.
Long noncoding RNAs (lncRNAs) play significant roles in various physiological and pathological processes via their interactions with biomolecules like DNA, RNA and protein. The existing in silico methods used for predicting the functions of lncRNA mainly rely on calculating the similarity of lncRNA or investigating whether an lncRNA can interact with a specific biomolecule or disease. In this work, we explored the functions of lncRNA from a different perspective: we presented a tool for predicting the interaction biomolecule type for a given lncRNA. For this purpose, we first investigated the main molecular mechanisms of the interactions of lncRNA-RNA, lncRNA-protein and lncRNA-DNA. Then, we developed an ensemble deep learning model: lncIBTP (lncRNA Interaction Biomolecule Type Prediction). This model predicted the interactions between lncRNA and different types of biomolecules. On the 5-fold cross-validation, the lncIBTP achieves average values of 0.7042 in accuracy, 0.7903 and 0.6421 in macro-average area under receiver operating characteristic curve and precision-recall curve, respectively, which illustrates the model effectiveness. Besides, based on the analysis of the collected published data and prediction results, we hypothesized that the characteristics of lncRNAs that interacted with DNA may be different from those that interacted with only RNA.
长链非编码 RNA（lncRNA）通过与 DNA、RNA 和蛋白质等生物分子的相互作用，在各种生理和病理过程中发挥重要作用。目前用于预测 lncRNA 功能的计算方法主要依赖于计算 lncRNA 的相似性，或研究 lncRNA 是否可以与特定的生物分子或疾病相互作用。在这项工作中，我们从不同的角度探讨了 lncRNA 的功能：我们提出了一种用于预测给定 lncRNA 相互作用生物分子类型的工具。为此，我们首先研究了 lncRNA-RNA、lncRNA-蛋白质和 lncRNA-DNA 相互作用的主要分子机制。然后，我们开发了一个集成深度学习模型：lncIBTP（lncRNA 相互作用生物分子类型预测）。该模型预测了 lncRNA 与不同类型生物分子之间的相互作用。在 5 折交叉验证中，lncIBTP 的准确率、宏平均 AUC 和精度-召回曲线的平均值分别为 0.7042、0.7903 和 0.6421，这说明了模型的有效性。此外，基于收集的已发表数据和预测结果的分析，我们假设与 DNA 相互作用的 lncRNA 的特征可能与仅与 RNA 相互作用的 lncRNA 的特征不同。
