School of Information Science and Engineering, Central South University, Changsha, 410083, China.
School of Information Science and Engineering, Qufu Normal University, Rizhao, 276826, China.
BMC Bioinformatics. 2019 Jan 9;20(1):16. doi: 10.1186/s12859-018-2586-3.
Long non-coding RNA (lncRNA) studies play an important role in the development, invasion, and metastasis of the tumor. The analysis and screening of the differential expression of lncRNAs in cancer and corresponding paracancerous tissues provides new clues for finding new cancer diagnostic indicators and improving the treatment. Predicting lncRNA-protein interactions is very important in the analysis of lncRNAs. This article proposes an Ant-Colony-Clustering-Based Bipartite Network (ACCBN) method and predicts lncRNA-protein interactions. The ACCBN method combines ant colony clustering and bipartite network inference to predict lncRNA-protein interactions.
A five-fold cross-validation method was used in the experimental test. The results show that the values of the evaluation indicators of ACCBN on the test set are significantly better after comparing the predictive ability of ACCBN with RWR, ProCF, LPIHN, and LPBNI method.
With the continuous development of biology, besides the research on the cellular process, the research on the interaction function between proteins becomes a new key topic of biology. The studies on protein-protein interactions had important implications for bioinformatics, clinical medicine, and pharmacology. However, there are many kinds of proteins, and their functions of interactions are complicated. Moreover, the experimental methods require time to be confirmed because it is difficult to estimate. Therefore, a viable solution is to predict protein-protein interactions efficiently with computers. The ACCBN method has a good effect on the prediction of protein-protein interactions in terms of sensitivity, precision, accuracy, and F1-score.
长链非编码 RNA（lncRNA）的研究在肿瘤的发生、浸润和转移中起着重要作用。分析和筛选癌症及相应癌旁组织中 lncRNA 的差异表达，为寻找新的癌症诊断指标、提高治疗效果提供了新的线索。预测 lncRNA-蛋白质相互作用在 lncRNA 分析中非常重要。本文提出了一种基于蚁群聚类的二分网络（ACCBN）方法，并预测 lncRNA-蛋白质相互作用。ACCBN 方法将蚁群聚类和二分网络推断相结合，预测 lncRNA-蛋白质相互作用。
在实验测试中使用了五重交叉验证方法。结果表明，与 RWR、ProCF、LPIHN 和 LPBNI 方法相比，ACCBN 在测试集上的评价指标值在比较 ACCBN 的预测能力后有显著提高。
随着生物学的不断发展，除了对细胞过程的研究外，蛋白质相互作用的研究成为生物学的一个新的关键课题。蛋白质-蛋白质相互作用的研究对生物信息学、临床医学和药理学具有重要意义。然而，蛋白质种类繁多，相互作用的功能复杂，而且实验方法需要时间来确认，因此，利用计算机有效地预测蛋白质-蛋白质相互作用是一种可行的解决方案。在敏感性、精度、准确性和 F1 评分方面，ACCBN 方法在预测蛋白质-蛋白质相互作用方面具有较好的效果。
