Jin Shuting, Zeng Xiangxiang, Xia Feng, Huang Wei, Liu Xiangrong
Brief Bioinform. 2021 Mar 22;22(2):1902-1917. doi: 10.1093/bib/bbaa043.
The increase in biological data and the formation of various biomolecule interaction databases enable us to obtain diverse biological networks. These biological networks provide a wealth of raw materials for further understanding of biological systems, the discovery of complex diseases and the search for therapeutic drugs. However, the increase in data also increases the difficulty of biological networks analysis. Therefore, algorithms that can handle large, heterogeneous and complex data are needed to better analyze the data of these network structures and mine their useful information. Deep learning is a branch of machine learning that extracts more abstract features from a larger set of training data. Through the establishment of an artificial neural network with a network hierarchy structure, deep learning can extract and screen the input information layer by layer and has representation learning ability. The improved deep learning algorithm can be used to process complex and heterogeneous graph data structures and is increasingly being applied to the mining of network data information. In this paper, we first introduce the used network data deep learning models. After words, we summarize the application of deep learning on biological networks. Finally, we discuss the future development prospects of this field.
生物数据的增加以及各种生物分子相互作用数据库的形成，使我们能够获得多样化的生物网络。这些生物网络为进一步理解生物系统、发现复杂疾病以及寻找治疗药物提供了丰富的原材料。然而，数据的增加也增加了生物网络分析的难度。因此，需要能够处理大规模、异构和复杂数据的算法，以更好地分析这些网络结构的数据并挖掘其中的有用信息。深度学习是机器学习的一个分支，它从更大的训练数据集中提取更抽象的特征。通过建立具有网络层次结构的人工神经网络，深度学习可以逐层提取和筛选输入信息，并具有表征学习能力。改进后的深度学习算法可用于处理复杂的异构图数据结构，并越来越多地应用于网络数据信息的挖掘。在本文中，我们首先介绍所使用的网络数据深度学习模型。之后，我们总结深度学习在生物网络上的应用。最后，我们讨论该领域未来的发展前景。
